Tuflow - User contributions [en]

TUFLOW Output Discussion

2026-04-08T03:15:02Z

Abrar.Alttahir: /* Why are the values of the mapped water level results (h_max) lower than the terrain elevations (DEM_Z)? */

= Common Questions Answered (FAQ)=
== Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)? ==
Slight differences are expected between the result grids (ASC or FLT) and mesh results (XMDF or DAT) due to small differences in the interpolation method.
* When outputting directly to FLT or ASC using the <tt>Map Output Format</tt> command, the output grid results are derived from both the cell centres and cell corners and the final results has those values interpolated to the centres of half cells (by default).
* When outputting XMDF (or DAT) format, the mesh results are derived from cell centres for depths / levels and cell sides for vector data. The final XMDF stores data interpolated to the cell corners and doesn’t have any memory of where the corner data were derived from and what those values were.
The differences can also be amplified when the rotation of the model is not perfectly north-south aligned (i.e., a non-zero orientation). 

== Why are grids processed with TUFLOW_to_GIS different from TUFLOW grids? ==
Slight differences are expected between the result grids (ASC or FLT) produced directly from TUFLOW and post-processed from the TUFLOW_to_GIS utility from XMDF or DAT due to differences in the interpolation approach.
Check [[TUFLOW_Output_Discussion#Why_are_grid_outputs_.28ASC.2C_FLT.29_slightly_different_from_mesh_outputs_.28XMDF.2C_DAT.29.3F | Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)?]] question above on differences between outputs direct from the TUFLOW Engine. 
When ASC/FLT results are sampled from the XMDF files with the TUFLOW_to_GIS utility, it uses less data points than when the grids are written directly from TUFLOW.

The differences can also be amplified when the rotation of the model is a non-zero value.
The extent of the grids is also different. When TUFLOW starts, it removes any redundant areas (if any) outside of 2d_code layer to reduce the calculation time:
* The extent of grids written directly from TUFLOW works with the unreduced area, encapsulating the entire rotated model domain in a north-south orientated rectangle.
* The extent of XMDF (or DAT) results is based on the reduced area. When the TUFLOW_to_GIS utility is used, the post-processed north-south aligned grids encapsulate the reduced model extent. The origin coordinates of such grids doesn't match the origin of the model (model domain), neither the grids written out from TUFLOW directly.

In general, a better interpolation of results is achieved when the grids are written directly from TUFLOW than when post-processed with TUFLOW_to_GIS utility. Importantly, when comparing results the recommendation is to be consistent and to use the same method for the duration of a project to ensure there are no differences as a consequence of the post-processing method. 

== Why is Z0 hazard coming directly from TUFLOW different to the one calculated from maximum velocity and depth? ==
The maximum velocity and maximum depth doesn’t necessarily occur at the same time. The Z0 hazard written directly from TUFLOW has the depth x velocity calculated every timestep and at the end outputs the maximums of all the timesteps. When using the velocity and depth grids the maximum velocity is always used with maximum depth. 
Outputting the Z0 hazard grid directly from TUFLOW is more accurate, however, if this is not an option, manual creation is an acceptable workaround. The general recommendation is to choose one method and keep it consistent for the length of the project. 
When calculating the Z0 hazard from the velocity and depth, the grids have already been interpolated by TUFLOW before the raster calculation (less data is used for the calculation), whereas the TUFLOW Z0 output is interpolated from the calculated data directly. Please also refer to the above question for more information [[TUFLOW_Output_Discussion#Why_are_grid_outputs_.28ASC.2C_FLT.29_slightly_different_from_mesh_outputs_.28XMDF.2C_DAT.29.3F | Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)?]]. 

== Why does hazard output (Z1, Z2, ...) show float values and not just integers? ==
The hazard values are calculated in cell corners and are in a form of an integer. When the values are processed into a grid, which has, by default, a resolution of half the cell size, the values are interpolated to the cell centres - four cell centre values per cell. Further interpolation occurs when the model has a non-zero orientation because grid files are north-south aligned. 

== Why are results from 2d_po features, POMM.csv and plots extracted from XMDF in GIS package not matching? ==
There are numerous reasons for the outputs to not be matching:
* Plot output:
** The 2d_po results are tracked at every <tt>Time Series Output Interval</tt> and saved into the PO.csv file. If the water level peaks in between the intervals, that maximum water level won’t be recorded in the PO.csv.
** The POMM.csv tracks maximums of PO features at every computational timestep. 
* Map output:
** The temporal XMDF (or DAT) results are tracked every <tt>Map Output Interval</tt> and interpolated to the cell corners.
** The maximum XMDF map output records the value at every computational timestep, however the final maximum map output is also being interpolated to the cell corners. 
** The extraction tools in GIS packages use interpolated data for the plot creation, e.g. less data than the TUFLOW direct plot output.
* Although the maximums from POMM.csv and the maximum map output will be very close, they won't match exactly.

Further notes for using post-processed flux line:
* No information is stored in the map output about whether the flow is upstream or downstream controlled.
* The staggered computational grid means there is always some interpolation occurring when projecting levels and velocities to the flux line. This will cause significant uncertainty in areas of sudden topographic change such as at an embankment. It is also the reason that two similar lines side by side may provide different outputs.
* Highly variable/complex flows, which are usually associated with highly variable topography, will add to the uncertainty when interpolating to the flux line.

The general recommendation is to use a lot of 2d_po features with reasonably small <tt>Time Series Output Interval</tt> to achieve better accuracy, they won't slow the simulation down. In case of flux lines, the 2d_po lines are using the actual computed fluxes across each cell face and will be an exact representation of the flow being calculated by the 2D solver. Post-processed fluxes should only be located in areas of little topographic change and even then, used as a rough guide. 

== Why are my TIF raster outputs not updated after a model rerun? ==
The GeoTIFF raster output is supported from the 2023-03 Release and onwards. It is considered an image by File Explorer and for some users, the 'Date' in file explorer defaults to show the 'Date Created' rather than the 'Date Modified'. If it appears that your results in the 'Results' folder (e.g. XMDF) are being updated however the results in the 'Results/grid' folder (e.g. TIF) aren't, this may be the reason. 
To confirm, turn on both 'Date Created' and 'Date Modified': 
[[File:TIF Date 01.png | 500px]] 
The 'Date Modified' column will show the date of the last model run: 
[[File:TIF Date 02.png | 500px]] 
 

== How do I apply high resolution (HR) output and what are the options? ==
See the [[TUFLOW_HR_Output | TUFLOW HR Output]] Wiki page for details. 

== Why are there differences between high resolution grid from TUFLOW and remapped outputs generated by the asc_to_asc utility? ==
Differences are expected between the direct HR output and the [[ASC_to_ASC#Remap | remap function]] in the asc_to_asc utility due to the different steps involved in the interpolation of water level results. 
The direct HR output is based on a triangulation of the water levels at the cell centres and corners. In comparison, the remap function involves two interpolation steps. The first is the interpolation from the cell centres and corners to the grid map output locations (by default, half the cell size). Then, the asc_to_asc utility interpolates the water level output to the target DEM grids with fine resolution: 
[[File:High-res-interpolation.jpg|600px]] 

The direct HR output tends to have better resolution in comparison to the remapped results. Due to the additional stages of interpolation involved with the remapped output, there is potential to lose definition in the results particularly in areas with steep slopes.

==How to create a maximum grid for datasets without direct maximum output from a TUFLOW run?==

For cumulative datasets without direct maximum output (e.g. cumulative infiltration, cumulative rainfall etc), the map output interval in the TCF needs to match the simulation end time in seconds. The end time output is when the maximum occurs for cumulative datasets, as it represents the cumulative results over the entire simulation duration.

For non-cumulative datasets without direct maximum output (e.g., Froude number, rainfall rate etc):
*Adjust the <tt>Map Output Interval</tt> in the TCF to a small value to ensure temporal variations are captured.
*Specify XMDF for <tt>Map Output Format</tt>.
*Identify the output ID number of the desired dataset within the .xmdf file:
<pre>"C:\TUFLOW\Releases\res_to_res\res_to_res_w64.exe" -xnfo results.xmdf</pre>
*Extract maximum values using the [[RES_to_RES#Introduction | RES to RES]] utility.
*Confirm the dataset's output ID number in the resulting (maxmax).xmdf file:
<pre>"C:\TUFLOW\Releases\res_to_res\res_to_res_w64.exe" -xnfo (maxmax).xmdf</pre>
*Convert the resulting dataset to a preferred grid format for GIS using the [[TUFLOW_to_GIS#Introduction | TUFLOW to GIS]] utility.

:Note: For non-cumulative datasets, this method does not capture the true maximum, however can be close enough with a fine map output interval.

== Why are the values of the mapped water level results (h_max) lower than the terrain elevations (DEM_Z)? ==

As of TUFLOW version 2025.1.1, there is a current limitation affecting result outputs for shallow sheet flow cells on steeper terrain in sub-grid sampling (SGS) enabled models. Direct rainfall models are particularly prone to producing substantial areas of shallow sheet flow.

In TUFLOW HPC, the cell averaged depth is used computationally and by default in the depth outputs (see below).

Water level outputs, however, assume that water fills from the bottom of the SGS cell’s storage curve. This water level result is recorded at the cell centre, interpolated to the cell corners, and then the gridded outputs are triangulated from these points (orange line). In sheet flow situations when the volume in that cell is small, the cell-centered water level may sit below the terrain (blue dot on diagram) if the cell is steep enough. This can result in a water level results sitting below the DEM_Z, even when there is a positive depth result. Note this does not affect the simulation computationally, only how the water level results are output. 
[[File:SGS_Sheetflow_Waterlevel.jpg|600px]]

== The modelled water surface level is below the SGS cell elevations. How should this result be interpreted or presented? ==
Below ground WSE in wet SGS cells is a known output behaviour in SGS enabled models and does not indicate an error in the hydraulic solution.

This occurs because TUFLOW reports water level assuming water fills from the lowest part of the SGS cell during output. As a result, cells may be wet while the reported WSE remains below the cell centre ground level.

This behaviour commonly occurs in the following situations:

'''Channel edge partial wetting'''

* Partially wet cells along channel banks may contain water that does not reach the cell centre elevation.
* In this case, compare the standard WSE with the standard DEM_Z check file to confirm consistency with terrain levels.

'''Shallow sheet flow from rainfall or side inflow'''

Cells may be wet but still report WSE below the cell centre ground level because the depth is small and distributed across the SGS terrain.

'''Presentation and reporting considerations'''

* A presentation method is to post process results by adding the cell averaged depth output <tt>SGS Depth Output</tt> <tt>==</tt><tt> CELL AVERAGE</tt> to the DEM elevation to derive an above ground water level.
* This approach can improve the visual representation of shallow flooding in sheet flow areas but may produce unrealistic water levels in fully wet or partially wet cells.
* Conditional IF logic may be required to choose where to present cell averaged depth plus DEM and where to retain the standard TUFLOW water level output.

== Why do the high-resolution (HR) mapped results have a significantly smaller extent than the standard (non-HR) grid outputs for a sub-grid-scale (SGS) direct rainfall HPC model? ==

The high resolution (HR) outputs are based on [[TUFLOW_HR_Output | intersecting]] the standard water level output results with the HR DEM_Z. Due to the [[TUFLOW_Output_Discussion#Why_are_the_values_of_the_mapped_water_level_results_.28h_max.29_lower_than_the_terrain_elevations_.28DEM_Z.29.3F | output limitation]] for sub-grid-scale (SGS) enabled models with shallow sheet flow (particularly direct rainfall models) on sufficiently steep terrain, water levels can be output lower than the DEM_Z terrain elevations. This is due to the assumption (for output purposes) that water fills the SGS cell’s storage curve from the bottom, which differs from the approach of cell-average depth used computationally in TUFLOW HPC and by default in depth outputs. When the HR water level results are processed, the full extent of the water level results are intersected with the HR DEM_Z and any water level results that are lower than the HR DEM_Z elevations due to the above effect are trimmed. The extent of the HR water level results are then used to trim the HR depth outputs.

== When should high-resolution (HR) outputs be used, and what are their limitations? ==
HR outputs are intended for visualisation and presentation because they retain sub-grid terrain detail but do not improve the underlying hydraulic calculations.

They are not suitable for assessment, benchmarking, or detailed analysis. Standard non-HR map outputs should be used for these purposes, and accuracy should be improved by refining the 2D cell size.
{{Tips Navigation
|uplink=[[TUFLOW_Modelling_Guidance | Back to TUFLOW Modelling Guidance]]
}}

TUFLOW Output Discussion

2026-04-08T03:13:46Z

Abrar.Alttahir: /* Why are the values of the mapped water level results (h_max) lower than the terrain elevations (DEM_Z)? */

= Common Questions Answered (FAQ)=
== Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)? ==
Slight differences are expected between the result grids (ASC or FLT) and mesh results (XMDF or DAT) due to small differences in the interpolation method.
* When outputting directly to FLT or ASC using the <tt>Map Output Format</tt> command, the output grid results are derived from both the cell centres and cell corners and the final results has those values interpolated to the centres of half cells (by default).
* When outputting XMDF (or DAT) format, the mesh results are derived from cell centres for depths / levels and cell sides for vector data. The final XMDF stores data interpolated to the cell corners and doesn’t have any memory of where the corner data were derived from and what those values were.
The differences can also be amplified when the rotation of the model is not perfectly north-south aligned (i.e., a non-zero orientation). 

== Why are grids processed with TUFLOW_to_GIS different from TUFLOW grids? ==
Slight differences are expected between the result grids (ASC or FLT) produced directly from TUFLOW and post-processed from the TUFLOW_to_GIS utility from XMDF or DAT due to differences in the interpolation approach.
Check [[TUFLOW_Output_Discussion#Why_are_grid_outputs_.28ASC.2C_FLT.29_slightly_different_from_mesh_outputs_.28XMDF.2C_DAT.29.3F | Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)?]] question above on differences between outputs direct from the TUFLOW Engine. 
When ASC/FLT results are sampled from the XMDF files with the TUFLOW_to_GIS utility, it uses less data points than when the grids are written directly from TUFLOW.

The differences can also be amplified when the rotation of the model is a non-zero value.
The extent of the grids is also different. When TUFLOW starts, it removes any redundant areas (if any) outside of 2d_code layer to reduce the calculation time:
* The extent of grids written directly from TUFLOW works with the unreduced area, encapsulating the entire rotated model domain in a north-south orientated rectangle.
* The extent of XMDF (or DAT) results is based on the reduced area. When the TUFLOW_to_GIS utility is used, the post-processed north-south aligned grids encapsulate the reduced model extent. The origin coordinates of such grids doesn't match the origin of the model (model domain), neither the grids written out from TUFLOW directly.

In general, a better interpolation of results is achieved when the grids are written directly from TUFLOW than when post-processed with TUFLOW_to_GIS utility. Importantly, when comparing results the recommendation is to be consistent and to use the same method for the duration of a project to ensure there are no differences as a consequence of the post-processing method. 

== Why is Z0 hazard coming directly from TUFLOW different to the one calculated from maximum velocity and depth? ==
The maximum velocity and maximum depth doesn’t necessarily occur at the same time. The Z0 hazard written directly from TUFLOW has the depth x velocity calculated every timestep and at the end outputs the maximums of all the timesteps. When using the velocity and depth grids the maximum velocity is always used with maximum depth. 
Outputting the Z0 hazard grid directly from TUFLOW is more accurate, however, if this is not an option, manual creation is an acceptable workaround. The general recommendation is to choose one method and keep it consistent for the length of the project. 
When calculating the Z0 hazard from the velocity and depth, the grids have already been interpolated by TUFLOW before the raster calculation (less data is used for the calculation), whereas the TUFLOW Z0 output is interpolated from the calculated data directly. Please also refer to the above question for more information [[TUFLOW_Output_Discussion#Why_are_grid_outputs_.28ASC.2C_FLT.29_slightly_different_from_mesh_outputs_.28XMDF.2C_DAT.29.3F | Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)?]]. 

== Why does hazard output (Z1, Z2, ...) show float values and not just integers? ==
The hazard values are calculated in cell corners and are in a form of an integer. When the values are processed into a grid, which has, by default, a resolution of half the cell size, the values are interpolated to the cell centres - four cell centre values per cell. Further interpolation occurs when the model has a non-zero orientation because grid files are north-south aligned. 

== Why are results from 2d_po features, POMM.csv and plots extracted from XMDF in GIS package not matching? ==
There are numerous reasons for the outputs to not be matching:
* Plot output:
** The 2d_po results are tracked at every <tt>Time Series Output Interval</tt> and saved into the PO.csv file. If the water level peaks in between the intervals, that maximum water level won’t be recorded in the PO.csv.
** The POMM.csv tracks maximums of PO features at every computational timestep. 
* Map output:
** The temporal XMDF (or DAT) results are tracked every <tt>Map Output Interval</tt> and interpolated to the cell corners.
** The maximum XMDF map output records the value at every computational timestep, however the final maximum map output is also being interpolated to the cell corners. 
** The extraction tools in GIS packages use interpolated data for the plot creation, e.g. less data than the TUFLOW direct plot output.
* Although the maximums from POMM.csv and the maximum map output will be very close, they won't match exactly.

Further notes for using post-processed flux line:
* No information is stored in the map output about whether the flow is upstream or downstream controlled.
* The staggered computational grid means there is always some interpolation occurring when projecting levels and velocities to the flux line. This will cause significant uncertainty in areas of sudden topographic change such as at an embankment. It is also the reason that two similar lines side by side may provide different outputs.
* Highly variable/complex flows, which are usually associated with highly variable topography, will add to the uncertainty when interpolating to the flux line.

The general recommendation is to use a lot of 2d_po features with reasonably small <tt>Time Series Output Interval</tt> to achieve better accuracy, they won't slow the simulation down. In case of flux lines, the 2d_po lines are using the actual computed fluxes across each cell face and will be an exact representation of the flow being calculated by the 2D solver. Post-processed fluxes should only be located in areas of little topographic change and even then, used as a rough guide. 

== Why are my TIF raster outputs not updated after a model rerun? ==
The GeoTIFF raster output is supported from the 2023-03 Release and onwards. It is considered an image by File Explorer and for some users, the 'Date' in file explorer defaults to show the 'Date Created' rather than the 'Date Modified'. If it appears that your results in the 'Results' folder (e.g. XMDF) are being updated however the results in the 'Results/grid' folder (e.g. TIF) aren't, this may be the reason. 
To confirm, turn on both 'Date Created' and 'Date Modified': 
[[File:TIF Date 01.png | 500px]] 
The 'Date Modified' column will show the date of the last model run: 
[[File:TIF Date 02.png | 500px]] 
 

== How do I apply high resolution (HR) output and what are the options? ==
See the [[TUFLOW_HR_Output | TUFLOW HR Output]] Wiki page for details. 

== Why are there differences between high resolution grid from TUFLOW and remapped outputs generated by the asc_to_asc utility? ==
Differences are expected between the direct HR output and the [[ASC_to_ASC#Remap | remap function]] in the asc_to_asc utility due to the different steps involved in the interpolation of water level results. 
The direct HR output is based on a triangulation of the water levels at the cell centres and corners. In comparison, the remap function involves two interpolation steps. The first is the interpolation from the cell centres and corners to the grid map output locations (by default, half the cell size). Then, the asc_to_asc utility interpolates the water level output to the target DEM grids with fine resolution: 
[[File:High-res-interpolation.jpg|600px]] 

The direct HR output tends to have better resolution in comparison to the remapped results. Due to the additional stages of interpolation involved with the remapped output, there is potential to lose definition in the results particularly in areas with steep slopes.

==How to create a maximum grid for datasets without direct maximum output from a TUFLOW run?==

For cumulative datasets without direct maximum output (e.g. cumulative infiltration, cumulative rainfall etc), the map output interval in the TCF needs to match the simulation end time in seconds. The end time output is when the maximum occurs for cumulative datasets, as it represents the cumulative results over the entire simulation duration.

For non-cumulative datasets without direct maximum output (e.g., Froude number, rainfall rate etc):
*Adjust the <tt>Map Output Interval</tt> in the TCF to a small value to ensure temporal variations are captured.
*Specify XMDF for <tt>Map Output Format</tt>.
*Identify the output ID number of the desired dataset within the .xmdf file:
<pre>"C:\TUFLOW\Releases\res_to_res\res_to_res_w64.exe" -xnfo results.xmdf</pre>
*Extract maximum values using the [[RES_to_RES#Introduction | RES to RES]] utility.
*Confirm the dataset's output ID number in the resulting (maxmax).xmdf file:
<pre>"C:\TUFLOW\Releases\res_to_res\res_to_res_w64.exe" -xnfo (maxmax).xmdf</pre>
*Convert the resulting dataset to a preferred grid format for GIS using the [[TUFLOW_to_GIS#Introduction | TUFLOW to GIS]] utility.

:Note: For non-cumulative datasets, this method does not capture the true maximum, however can be close enough with a fine map output interval.

== Why are the values of the mapped water level results (h_max) lower than the terrain elevations (DEM_Z)? ==

As of TUFLOW version 2025.1.1, there is a current limitation affecting result outputs for shallow sheet flow cells on steeper terrain in sub-grid-scale (SGS) enabled models. Direct rainfall models are particularly prone to producing substantial areas of shallow sheet flow.

In TUFLOW HPC, the cell averaged depth is used computationally and by default in the depth outputs (see below).

Water level outputs, however, assume that water fills from the bottom of the SGS cell’s storage curve. This water level result is recorded at the cell centre, interpolated to the cell corners, and then the gridded outputs are triangulated from these points (orange line). In sheet flow situations when the volume in that cell is small, the cell-centered water level may sit below the terrain (blue dot on diagram) if the cell is steep enough. This can result in a water level results sitting below the DEM_Z, even when there is a positive depth result. Note this does not affect the simulation computationally, only how the water level results are output. 
[[File:SGS_Sheetflow_Waterlevel.jpg|600px]]

== The modelled water surface level is below the SGS cell elevations. How should this result be interpreted or presented? ==
Below ground WSE in wet SGS cells is a known output behaviour in SGS enabled models and does not indicate an error in the hydraulic solution.

This occurs because TUFLOW reports water level assuming water fills from the lowest part of the SGS cell during output. As a result, cells may be wet while the reported WSE remains below the cell centre ground level.

This behaviour commonly occurs in the following situations:

'''Channel edge partial wetting'''

* Partially wet cells along channel banks may contain water that does not reach the cell centre elevation.
* In this case, compare the standard WSE with the standard DEM_Z check file to confirm consistency with terrain levels.

'''Shallow sheet flow from rainfall or side inflow'''

Cells may be wet but still report WSE below the cell centre ground level because the depth is small and distributed across the SGS terrain.

'''Presentation and reporting considerations'''

* A presentation method is to post process results by adding the cell averaged depth output <tt>SGS Depth Output</tt> <tt>==</tt><tt> CELL AVERAGE</tt> to the DEM elevation to derive an above ground water level.
* This approach can improve the visual representation of shallow flooding in sheet flow areas but may produce unrealistic water levels in fully wet or partially wet cells.
* Conditional IF logic may be required to choose where to present cell averaged depth plus DEM and where to retain the standard TUFLOW water level output.

== Why do the high-resolution (HR) mapped results have a significantly smaller extent than the standard (non-HR) grid outputs for a sub-grid-scale (SGS) direct rainfall HPC model? ==

The high resolution (HR) outputs are based on [[TUFLOW_HR_Output | intersecting]] the standard water level output results with the HR DEM_Z. Due to the [[TUFLOW_Output_Discussion#Why_are_the_values_of_the_mapped_water_level_results_.28h_max.29_lower_than_the_terrain_elevations_.28DEM_Z.29.3F | output limitation]] for sub-grid-scale (SGS) enabled models with shallow sheet flow (particularly direct rainfall models) on sufficiently steep terrain, water levels can be output lower than the DEM_Z terrain elevations. This is due to the assumption (for output purposes) that water fills the SGS cell’s storage curve from the bottom, which differs from the approach of cell-average depth used computationally in TUFLOW HPC and by default in depth outputs. When the HR water level results are processed, the full extent of the water level results are intersected with the HR DEM_Z and any water level results that are lower than the HR DEM_Z elevations due to the above effect are trimmed. The extent of the HR water level results are then used to trim the HR depth outputs.

== When should high-resolution (HR) outputs be used, and what are their limitations? ==
HR outputs are intended for visualisation and presentation because they retain sub-grid terrain detail but do not improve the underlying hydraulic calculations.

They are not suitable for assessment, benchmarking, or detailed analysis. Standard non-HR map outputs should be used for these purposes, and accuracy should be improved by refining the 2D cell size.
{{Tips Navigation
|uplink=[[TUFLOW_Modelling_Guidance | Back to TUFLOW Modelling Guidance]]
}}

Hardware Selection Advice

2026-03-23T04:12:05Z

Abrar.Alttahir: /* RAM Reliability (ECC vs non-ECC) */

This page provides general hardware advice for running TUFLOW models on GPU or CPU. 
[[File: Hardware_Configuration_001.jpg ||450px|right]]

=Introduction=
We often get asked about the optimum computing setup to run TUFLOW models. While every model is different and will interact differently with your hardware there is some general advice that we can offer. Note that recommendations focus specifically on running TUFLOW. It is highly recommend to consult your IT team to confirm that all components of the machine are fully capable of supporting your intended uses and meet your requirements for quality, speed, and durability. 

In the sections below you will find more detailed advice on GPU and CPU but generally: 

* The amount of RAM in the computer will be the limiter for the size of model you can run. This applies to CPU RAM (TUFLOW Classic, TUFLOW FV and TUFLOW HPC with Hardware == CPU) and also GPU RAM (TUFLOW HPC and TUFLOW FV with Hardware == GPU). If available RAM becomes a limitation, users should also investigate improvements to their model configuration to reduce RAM requirements (see [[TUFLOW Simulation Speed | TUFLOW Simulation Speed]]).

* The processing speed of the CPU, the architecture, cache size, speed and number of processors play a role.
* For GPU simulations, the number of CUDA cores, the core speed, GPU card architecture, memory speed and interfacing with the motherboard PCI lanes and CPU are all important.
* The system must be well cooled to avoid throttling (meaning reduction of clock speeds to reduce heating), and have sufficient and reliable power supply. Should upgrades to the system be expected in the future (such as adding a second GPU card), then consider configuring these components to avoid future limitations. 
For information on minimum and recommended system requirement, see [[System_Requirements | System Requirements]].

To discover a computer's NVIDIA GPU hardware, see [[Console_Window_GPU_Usage | NVIDIA GPU Hardware and Usage]]. 

=The TUFLOW Software Suite=
The TUFLOW Software suite has a range of solvers. Each interact differently with your hardware so pairing the correct solver (or the range of solvers you want to run) and hardware is an important consideration. A brief summary of each solver's needs is provided as follows: 
*TUFLOW Classic: A single model run can only use the CPU and cannot be run across multiple CPU cores or GPU hardware. In general terms: The maximum model size is dependent on the available CPU RAM and the runtime is driven by the CPU speed, architecture and cache size.
* TUFLOW HPC - Run on CPU Hardware: A single model run uses the CPU and is parallelised to run across multiple cores. In general terms: The maximum model size is dependent on the available CPU RAM and the runtime is driven by the CPU speed, the number of cores available to be run in parallel, architecture and cache size.
*TUFLOW HPC - Run on GPU Hardware: A single model run uses the GPU(s) for computation. In general terms: The maximum model size is dependent on the available GPU and CPU RAM and the runtime is driven by the CUDA core speed, the number of CUDA cores available and the GPU architecture. GPU performance is complex and is not easily inferred from GPU clock speed and number of cores, it is also very dependent on the ‘generation’ or architecture of GPU. As TUFLOW HPC requires some data exchange between GPU and CPU, the motherboard bus speeds and CPU speeds also play a role but typically a much lesser role compared to the GPU CUDA compute.
*TUFLOW FV - Run on CPU Hardware: A single model run uses CPU and is parallelised to run across multiple cores. In general terms: The maximum model size is dependent on the available CPU RAM and the runtime is determined by the CPU speed, the number of cores available to be run in parallel, chip architecture and cache size.
*TUFLOW FV - Run on GPU Hardware: A single model run uses the GPU(s) for computation. In general terms: The maximum model size is dependent on the available GPU and CPU RAM and the runtime is driven by the CUDA core speed, the number of CUDA cores available and the GPU architecture. GPU performance is complex and is not easily inferred from GPU clock speed and number of cores, it is also very dependent on the ‘generation’ or architecture of GPU. As TUFLOW FV requires some data exchange between GPU and CPU, the motherboard bus speeds and CPU speeds also play a role but typically a much lesser role compared to the GPU CUDA compute. 

The [[Hardware_Benchmarking_-_Results#CPU_Results | Hardware Benchmarking]] page shows recently run combinations of GPU, CPU and RAM. These can be compared with the system intended for purchase. The recommendation is to seek advise from an appropriate computer hardware vendor who can advise on the compatibility and optimisation of the setup.

=GPU Advice=
TUFLOW HPC on GPU Hardware is typically our fastest solver for 1D/2D pipe and floodplain simulations.
* TUFLOW HPC supports CUDA enabled NVIDIA GPU cards. For list of supported CUDA enabled graphics cards please visit the [https://developer.nvidia.com/cuda-gpus NVIDIA website].
*To discover a computer's NVIDIA GPU hardware, see [[Console_Window_GPU_Usage | NVIDIA GPU Hardware and Usage]].
*TUFLOW HPC on GPU Hardware can be run in either single or double precision. However, for the vast majority of flood applications single precision is sufficient. We typically run our models on single precision. If you are unsure we recommend running with both the single and double precision solvers and comparing your results.
The precision solver you require will determine the type of GPU card that is best suited for your compute. For any given generation/architecture of cards, the “gaming” cards such as the GTX GeForce and RTX provide excellent single precision performance – typically comparable to that of the “scientific” cards such as the Tesla series. If double precision is required then the scientific cards are substantially faster, but these are also significantly more expensive. The Quadro series cards sit in between for both double precision performance and cost. When checking the specifications of the card it should provide you with a breakdown of the single and double precision throughput in flops. Single precision compute is typically sufficient for TUFLOW HPC modelling.

For the higher end GPU cards, users may wish to consider server-based computers rather than workstations, and also weigh the cost of an extra TUFLOW licence against the cost of the high end hardware.

===GPU RAM===
RAM is the computer memory required to store all of the model data used during the computation. A computer has CPU RAM which is located on the motherboard and accessed from the CPU, and it has GPU RAM which is located on the GPU device and accessed from the GPU. The two memory storage systems are physically separate.
The amount of GPU RAM is one of two factors that will determine the size of the model that can be run (the other being CPU RAM). As a rule of thumb, approximately 5 million cells can be run per gigabyte (GB) of GPU RAM depending on the model features, e.g. a model with infiltration requires more memory due to the extra variables needed for the infiltration calculation.

===CPU RAM===
TUFLOW HPC on GPU hardware still uses the CPU to compute and store data (in CPU RAM) during model initialisation and for all 1D calculations. While we are working on improving our CPU RAM usage, currently we tend to find that CPU RAM is often the limiter to the size of the model domain you can run, particularly if using running over multiple GPU cards. During initialisation and simulation a model will typically require 4-6 times the amount of CPU RAM relative to GPU RAM. As an example, a model that utilises 11GB of GPU RAM (typical memory for high-end gaming card, and corresponds to about a 50 million cell model) the CPU RAM required during initialisation will typically be in range 44GB to 66GB. A model that fully utilises two 11 GB GPUs (i.e. a 100 million cell model) may require as much as 128GB of CPU RAM during initialisation. Note that anything more than 256GB of CPU RAM will exceed the limitations of consumer chipsets that are available in 2025 and requires more expensive workstation hardware - additionally, users should consult a hardware expert to check limitations of specific hardware.

=== RAM Reliability (ECC vs non-ECC) ===
ECC (Error-Correcting Code) RAM detects and corrects memory errors, improving reliability, while non-ECC cannot. There is some misinformation about random cosmic ray bit flips. Large field studies show errors are usually caused by physical faults in specific DIMMs (Dual In-line Memory Modules, the removable RAM sticks), not uniform random events. Most DIMMs experience no errors, while a small number produce the vast majority of faults. Modern DDR5 memory also includes on-die correction that silently fixes some errors before they leave the chip.

A failing DIMM on a non-ECC system is more likely to cause crashes or obvious corruption than a silent incorrect result. In numerical solvers, bit flips often trigger instability or failure rather than plausible but wrong outputs. For a single TUFLOW workstation, ECC is generally not required solely to protect result quality, though it may be beneficial for servers, critical workloads, or environments operating many machines.

If additional confidence is desired, run a memory test (for example Memtest86+) for multiple passes after installation. Consistent errors indicate defective hardware that should be replaced.

===CUDA Cores, GPU Clock speed, and FLOPs ===
One way of reporting a GPU card's throughput is in Floating Point Operations per second (FLOPs). The more FLOPs, the more calculations that can get crunched per second and the faster the model should run. For any given generation of GPU, FLOPs are approximately proportional to number of CUDA cores times the GPU clock speed. However, there have been significant improvements in GPU architecture since the inception of CUDA, and this has contributed to increases in overall FLOPs performance beyond just the increases in cores and clock speed that have occurred over this time.

===Multiple GPUs===
TUFLOW can use multiple GPU cards on a machine to run a single model (TUFLOW FV can currently use a single GPU only). This is useful for models that are too large for a single GPU, or for running a model as quickly as possible. In general terms the run time benefit of using multiple cards increases with model size.
*TUFLOW HPC-GPU does not support SLI for inter-GPU communications.
*It does (as of build 2020-01-AA) auto detect and utilise peer-to-peer access over NVLink or PCI bus on the motherboard. Note that not all GPUs support peer-to-peer access.
**PCI bus - this method requires cards that supports TCC driver mode and all cards must be in TCC driver mode. As TUFLOW primarily relies on GPU CUDA capabilities, the impact of using higher or lower PCI slot option is minimal.
**NVLink - high-end compute cards can have up to 8 cards talking to each other through a high-spec NVLink, but many of the less expensive cards are limited to only having two connected together over a dual socket NVLink.
*Models may still be run across multiple GPUs even if a NVLink is not present and the GPUs do not support peer-to-peer access. In this case HPC reverts to exchanging the domain boundary data between the GPUs via the CPU. The memory bandwidth between the GPU and the main system is not a critical bottleneck for TUFLOW.
*When using multiple GPUs it is best to use cards of similar memory and performance. While it is possible (as of build 2020-01-AA) to re-balance a model over multiple GPUs, we do not recommend using cards with vastly disparate performance.
*Sufficient cooling and power supply should be considered if multiple cards are used. When installed in adjacent PCI slots, the preference is to use rear vented cards rather than side vented to avoid blowing hot air onto the neighbouring cards (which could lead to overheating).

===GPU Performance Comparison===
Extensive GPU hardware speed comparison testing has been completed using TUFLOW's standardised hardware benchmarking dataset. Details for the benchmarking are available via the [[Hardware_Benchmarking | Hardware Benchmarking]] page. Review the GPU benchmarking runtime results table to compare the speed performance of different cards. If your GPU card is not listed in the result dataset please download and run the benchmarking dataset, and provide the result summary to [mailto:support@tuflow.com support@tuflow.com]. We will add the details to the runtime results table. 

External videocard benchmark websites can be used to compare GPU cards, for example, [https://www.videocardbenchmark.net/high_end_gpus.html PassMark Software - Video Card (GPU) Benchmarks] is an excellent performance guide. Note that PassMark may not be representative for the highest end cards, for TUFLOW. GPU are complex devices, newer cards may not perform as well on PassMark's benchmarks for criteria consumers buy GPUs for (games, video editing, etc.), even though the cards may well perform a lot better for TUFLOW.
 

=CPU Advice=
In general terms a more recent architecture, higher clock speed CPU with a large cache will perform better than a slower clock speed chip. This section discusses CPU RAM, RAM speed, Processor frequency, Multi-core processing and hyper-threading.

===CPU RAM===
The amount of CPU RAM will determine the size of the model that can be run or a number of models that can be run at one time.
Faster RAM will result in quicker runtimes, however this is usually a secondary consideration to chip speed, cache size and architecture.

===CPU Cores ===
*TUFLOW HPC - Run on GPU Hardware: The parallel processing is being done on the GPU card. However, TUFLOW HPC-GPU still uses the CPU for model initialisation and for 1D calculations. If multiple GPU cards are used, TUFLOW will use the equivalent number of CPU threads for controlling the GPUs and migrating data. So for a machine dedicated to HPC-GPU modelling, the number of CPU cores should be higher than the number of installed GPUs.
*TUFLOW HPC - Run on CPU Hardware: HPC model can also be run on multiple CPU cores. For the comparison of simulation speed, please refer to [[Hardware_Benchmarking_Topic_HPC_on_CPU_vs_GPU | HPC on CPU vs GPU]].
*TUFLOW Classic: TUFLOW Classic simulation can only use one CPU core due to the implicit nature of the numerical solution. More CPU cores will enable running more simulations at the same time most efficiently.

===Hyperthreading===
https://fvwiki.tuflow.com/index.php?title=TUFLOW_FV_Parallel_Computing

===Processor Frequency and RAM Frequency===
The frequency directly affects the run times. In general, the higher the frequency, the faster the model runs.

===CPU Performance Comparison===
Extensive CPU hardware speed comparison testing has been completed using TUFLOW's standardised hardware benchmarking dataset. Details for the benchmarking are available via the [[Hardware_Benchmarking| Hardware Benchmarking]] page of the Wiki. Review the CPU benchmarking runtime results table to compare the speed performance of different chips. If your chip is not listed in the result dataset please download and run the benchmarking dataset, and provide the result summary to [mailto:support@tuflow.com support@tuflow.com]. We will add the details to the runtime results table.
 

=Storage Advice=
Solid state hard drives are preferred for temporary storage as they are faster to write to than traditional hard drives. Large data files can then be transferred to a more permanent location. 
 

{{Tips Navigation
|uplink=[[Main_Page| TUFLOW Main Page]]
}}

Hardware Selection Advice

2026-03-23T04:00:55Z

Abrar.Alttahir: /* GPU Advice */

This page provides general hardware advice for running TUFLOW models on GPU or CPU. 
[[File: Hardware_Configuration_001.jpg ||450px|right]]

=Introduction=
We often get asked about the optimum computing setup to run TUFLOW models. While every model is different and will interact differently with your hardware there is some general advice that we can offer. Note that recommendations focus specifically on running TUFLOW. It is highly recommend to consult your IT team to confirm that all components of the machine are fully capable of supporting your intended uses and meet your requirements for quality, speed, and durability. 

In the sections below you will find more detailed advice on GPU and CPU but generally: 

* The amount of RAM in the computer will be the limiter for the size of model you can run. This applies to CPU RAM (TUFLOW Classic, TUFLOW FV and TUFLOW HPC with Hardware == CPU) and also GPU RAM (TUFLOW HPC and TUFLOW FV with Hardware == GPU). If available RAM becomes a limitation, users should also investigate improvements to their model configuration to reduce RAM requirements (see [[TUFLOW Simulation Speed | TUFLOW Simulation Speed]]).

* The processing speed of the CPU, the architecture, cache size, speed and number of processors play a role.
* For GPU simulations, the number of CUDA cores, the core speed, GPU card architecture, memory speed and interfacing with the motherboard PCI lanes and CPU are all important.
* The system must be well cooled to avoid throttling (meaning reduction of clock speeds to reduce heating), and have sufficient and reliable power supply. Should upgrades to the system be expected in the future (such as adding a second GPU card), then consider configuring these components to avoid future limitations. 
For information on minimum and recommended system requirement, see [[System_Requirements | System Requirements]].

To discover a computer's NVIDIA GPU hardware, see [[Console_Window_GPU_Usage | NVIDIA GPU Hardware and Usage]]. 

=The TUFLOW Software Suite=
The TUFLOW Software suite has a range of solvers. Each interact differently with your hardware so pairing the correct solver (or the range of solvers you want to run) and hardware is an important consideration. A brief summary of each solver's needs is provided as follows: 
*TUFLOW Classic: A single model run can only use the CPU and cannot be run across multiple CPU cores or GPU hardware. In general terms: The maximum model size is dependent on the available CPU RAM and the runtime is driven by the CPU speed, architecture and cache size.
* TUFLOW HPC - Run on CPU Hardware: A single model run uses the CPU and is parallelised to run across multiple cores. In general terms: The maximum model size is dependent on the available CPU RAM and the runtime is driven by the CPU speed, the number of cores available to be run in parallel, architecture and cache size.
*TUFLOW HPC - Run on GPU Hardware: A single model run uses the GPU(s) for computation. In general terms: The maximum model size is dependent on the available GPU and CPU RAM and the runtime is driven by the CUDA core speed, the number of CUDA cores available and the GPU architecture. GPU performance is complex and is not easily inferred from GPU clock speed and number of cores, it is also very dependent on the ‘generation’ or architecture of GPU. As TUFLOW HPC requires some data exchange between GPU and CPU, the motherboard bus speeds and CPU speeds also play a role but typically a much lesser role compared to the GPU CUDA compute.
*TUFLOW FV - Run on CPU Hardware: A single model run uses CPU and is parallelised to run across multiple cores. In general terms: The maximum model size is dependent on the available CPU RAM and the runtime is determined by the CPU speed, the number of cores available to be run in parallel, chip architecture and cache size.
*TUFLOW FV - Run on GPU Hardware: A single model run uses the GPU(s) for computation. In general terms: The maximum model size is dependent on the available GPU and CPU RAM and the runtime is driven by the CUDA core speed, the number of CUDA cores available and the GPU architecture. GPU performance is complex and is not easily inferred from GPU clock speed and number of cores, it is also very dependent on the ‘generation’ or architecture of GPU. As TUFLOW FV requires some data exchange between GPU and CPU, the motherboard bus speeds and CPU speeds also play a role but typically a much lesser role compared to the GPU CUDA compute. 

The [[Hardware_Benchmarking_-_Results#CPU_Results | Hardware Benchmarking]] page shows recently run combinations of GPU, CPU and RAM. These can be compared with the system intended for purchase. The recommendation is to seek advise from an appropriate computer hardware vendor who can advise on the compatibility and optimisation of the setup.

=GPU Advice=
TUFLOW HPC on GPU Hardware is typically our fastest solver for 1D/2D pipe and floodplain simulations.
* TUFLOW HPC supports CUDA enabled NVIDIA GPU cards. For list of supported CUDA enabled graphics cards please visit the [https://developer.nvidia.com/cuda-gpus NVIDIA website].
*To discover a computer's NVIDIA GPU hardware, see [[Console_Window_GPU_Usage | NVIDIA GPU Hardware and Usage]].
*TUFLOW HPC on GPU Hardware can be run in either single or double precision. However, for the vast majority of flood applications single precision is sufficient. We typically run our models on single precision. If you are unsure we recommend running with both the single and double precision solvers and comparing your results.
The precision solver you require will determine the type of GPU card that is best suited for your compute. For any given generation/architecture of cards, the “gaming” cards such as the GTX GeForce and RTX provide excellent single precision performance – typically comparable to that of the “scientific” cards such as the Tesla series. If double precision is required then the scientific cards are substantially faster, but these are also significantly more expensive. The Quadro series cards sit in between for both double precision performance and cost. When checking the specifications of the card it should provide you with a breakdown of the single and double precision throughput in flops. Single precision compute is typically sufficient for TUFLOW HPC modelling.

For the higher end GPU cards, users may wish to consider server-based computers rather than workstations, and also weigh the cost of an extra TUFLOW licence against the cost of the high end hardware.

===GPU RAM===
RAM is the computer memory required to store all of the model data used during the computation. A computer has CPU RAM which is located on the motherboard and accessed from the CPU, and it has GPU RAM which is located on the GPU device and accessed from the GPU. The two memory storage systems are physically separate.
The amount of GPU RAM is one of two factors that will determine the size of the model that can be run (the other being CPU RAM). As a rule of thumb, approximately 5 million cells can be run per gigabyte (GB) of GPU RAM depending on the model features, e.g. a model with infiltration requires more memory due to the extra variables needed for the infiltration calculation.

===CPU RAM===
TUFLOW HPC on GPU hardware still uses the CPU to compute and store data (in CPU RAM) during model initialisation and for all 1D calculations. While we are working on improving our CPU RAM usage, currently we tend to find that CPU RAM is often the limiter to the size of the model domain you can run, particularly if using running over multiple GPU cards. During initialisation and simulation a model will typically require 4-6 times the amount of CPU RAM relative to GPU RAM. As an example, a model that utilises 11GB of GPU RAM (typical memory for high-end gaming card, and corresponds to about a 50 million cell model) the CPU RAM required during initialisation will typically be in range 44GB to 66GB. A model that fully utilises two 11 GB GPUs (i.e. a 100 million cell model) may require as much as 128GB of CPU RAM during initialisation. Note that anything more than 256GB of CPU RAM will exceed the limitations of consumer chipsets that are available in 2025 and requires more expensive workstation hardware - additionally, users should consult a hardware expert to check limitations of specific hardware.

=== RAM Reliability (ECC vs non-ECC) ===
ECC (Error-Correcting Code) RAM detects and corrects memory errors, improving reliability, while non-ECC cannot. There is significant misinformation about random “cosmic ray” bit flips. Large field studies show errors are usually caused by physical faults in specific DIMMs (Dual In-line Memory Modules, the removable RAM sticks), not uniform random events. Most DIMMs experience no errors, while a small number produce the vast majority of faults. Modern DDR5 memory also includes on-die correction that silently fixes some errors before they leave the chip.

A failing DIMM on a non-ECC system is more likely to cause crashes or obvious corruption than a silent incorrect result. In numerical solvers, bit flips often trigger instability or failure rather than plausible but wrong outputs. For a single TUFLOW workstation, ECC is generally not required solely to protect result quality, though it may be beneficial for servers, critical workloads, or environments operating many machines.

If additional confidence is desired, run a memory test (for example Memtest86+) for multiple passes after installation. Consistent errors indicate defective hardware that should be replaced.

===CUDA Cores, GPU Clock speed, and FLOPs ===
One way of reporting a GPU card's throughput is in Floating Point Operations per second (FLOPs). The more FLOPs, the more calculations that can get crunched per second and the faster the model should run. For any given generation of GPU, FLOPs are approximately proportional to number of CUDA cores times the GPU clock speed. However, there have been significant improvements in GPU architecture since the inception of CUDA, and this has contributed to increases in overall FLOPs performance beyond just the increases in cores and clock speed that have occurred over this time.

===Multiple GPUs===
TUFLOW can use multiple GPU cards on a machine to run a single model (TUFLOW FV can currently use a single GPU only). This is useful for models that are too large for a single GPU, or for running a model as quickly as possible. In general terms the run time benefit of using multiple cards increases with model size.
*TUFLOW HPC-GPU does not support SLI for inter-GPU communications.
*It does (as of build 2020-01-AA) auto detect and utilise peer-to-peer access over NVLink or PCI bus on the motherboard. Note that not all GPUs support peer-to-peer access.
**PCI bus - this method requires cards that supports TCC driver mode and all cards must be in TCC driver mode. As TUFLOW primarily relies on GPU CUDA capabilities, the impact of using higher or lower PCI slot option is minimal.
**NVLink - high-end compute cards can have up to 8 cards talking to each other through a high-spec NVLink, but many of the less expensive cards are limited to only having two connected together over a dual socket NVLink.
*Models may still be run across multiple GPUs even if a NVLink is not present and the GPUs do not support peer-to-peer access. In this case HPC reverts to exchanging the domain boundary data between the GPUs via the CPU. The memory bandwidth between the GPU and the main system is not a critical bottleneck for TUFLOW.
*When using multiple GPUs it is best to use cards of similar memory and performance. While it is possible (as of build 2020-01-AA) to re-balance a model over multiple GPUs, we do not recommend using cards with vastly disparate performance.
*Sufficient cooling and power supply should be considered if multiple cards are used. When installed in adjacent PCI slots, the preference is to use rear vented cards rather than side vented to avoid blowing hot air onto the neighbouring cards (which could lead to overheating).

===GPU Performance Comparison===
Extensive GPU hardware speed comparison testing has been completed using TUFLOW's standardised hardware benchmarking dataset. Details for the benchmarking are available via the [[Hardware_Benchmarking | Hardware Benchmarking]] page. Review the GPU benchmarking runtime results table to compare the speed performance of different cards. If your GPU card is not listed in the result dataset please download and run the benchmarking dataset, and provide the result summary to [mailto:support@tuflow.com support@tuflow.com]. We will add the details to the runtime results table. 

External videocard benchmark websites can be used to compare GPU cards, for example, [https://www.videocardbenchmark.net/high_end_gpus.html PassMark Software - Video Card (GPU) Benchmarks] is an excellent performance guide. Note that PassMark may not be representative for the highest end cards, for TUFLOW. GPU are complex devices, newer cards may not perform as well on PassMark's benchmarks for criteria consumers buy GPUs for (games, video editing, etc.), even though the cards may well perform a lot better for TUFLOW.
 

=CPU Advice=
In general terms a more recent architecture, higher clock speed CPU with a large cache will perform better than a slower clock speed chip. This section discusses CPU RAM, RAM speed, Processor frequency, Multi-core processing and hyper-threading.

===CPU RAM===
The amount of CPU RAM will determine the size of the model that can be run or a number of models that can be run at one time.
Faster RAM will result in quicker runtimes, however this is usually a secondary consideration to chip speed, cache size and architecture.

===CPU Cores ===
*TUFLOW HPC - Run on GPU Hardware: The parallel processing is being done on the GPU card. However, TUFLOW HPC-GPU still uses the CPU for model initialisation and for 1D calculations. If multiple GPU cards are used, TUFLOW will use the equivalent number of CPU threads for controlling the GPUs and migrating data. So for a machine dedicated to HPC-GPU modelling, the number of CPU cores should be higher than the number of installed GPUs.
*TUFLOW HPC - Run on CPU Hardware: HPC model can also be run on multiple CPU cores. For the comparison of simulation speed, please refer to [[Hardware_Benchmarking_Topic_HPC_on_CPU_vs_GPU | HPC on CPU vs GPU]].
*TUFLOW Classic: TUFLOW Classic simulation can only use one CPU core due to the implicit nature of the numerical solution. More CPU cores will enable running more simulations at the same time most efficiently.

===Hyperthreading===
https://fvwiki.tuflow.com/index.php?title=TUFLOW_FV_Parallel_Computing

===Processor Frequency and RAM Frequency===
The frequency directly affects the run times. In general, the higher the frequency, the faster the model runs.

===CPU Performance Comparison===
Extensive CPU hardware speed comparison testing has been completed using TUFLOW's standardised hardware benchmarking dataset. Details for the benchmarking are available via the [[Hardware_Benchmarking| Hardware Benchmarking]] page of the Wiki. Review the CPU benchmarking runtime results table to compare the speed performance of different chips. If your chip is not listed in the result dataset please download and run the benchmarking dataset, and provide the result summary to [mailto:support@tuflow.com support@tuflow.com]. We will add the details to the runtime results table.
 

=Storage Advice=
Solid state hard drives are preferred for temporary storage as they are faster to write to than traditional hard drives. Large data files can then be transferred to a more permanent location. 
 

{{Tips Navigation
|uplink=[[Main_Page| TUFLOW Main Page]]
}}

Hardware Benchmarking - Results

2026-03-23T03:16:08Z

Abrar.Alttahir: /* High End GPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"

! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | #
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|1
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||128||5200||32.7||108.6||141.3||JB1
|-
|2
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||64||5600||36.7||114.2||150.9||TM1
|-
|3
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs), ~3.0GHz
|3
|32
|4800
|41.9
|122.4
|164.3
|RA1
|-
|4
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||2.0||128||4000||41.2||124.1||165.3||AW1
|-
|5
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs)||3.2||128||4800||38.5||127.0||165.5||SB1
|-
|6
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz||4.5||64||4800||33.4||136.0||169.4||PSM
|-
|7
|12th Gen Intel(R) Core(TM) i9-12900K||3.2||128||3200||40.7||129.9||170.6||DD2
|-
|8
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz||2.1||64||4800||41||137.2||178.2||RB1
|-
|9
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs) @ ~4.2GHz||4.2||32||4800||35.5||162.8||198.3||LC1
|-
|10
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs)||2.5||16||3200||48.4||170.5||218.9||TS1
|-
|11
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz||3.2||64||4800||53.3||169.1||222.4||CHR
|-
|12
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|13
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|14
|AMD Ryzen 5 7600X 6-Core Processor||4.7||32||4800||39.0||175.7||214.7||HP1
|-
|15
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|16
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|17
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|18
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|19
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||3.6||64||2133||62.7||191.1||253.8||PA1
|-
|20
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|21
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|22
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|23
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|3.2
|450
|
|50.0
|211.6
|261.6
|YW1
|-
|24
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|25
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|26
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||3.2||56||N/A||49.1||218.9||268||RBR
|-
|27
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|28
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|29
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|30
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|31
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|32
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|33
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|34
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||32||2113||68.2||217.9||286.1||HP2
|-
|35
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|36
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|37
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|38
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|39
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|40
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||3.5||128||2666||66.3||234.7||301||JG3
|-
|41
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|42
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|43
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|44
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|45
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|2.6
|32
|3200
|66.6
|243.3
|309.9
|SK1
|-
|46
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|47
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|48
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|49
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz||1.4||16||7467||173.8||152.8||326.6||AL1
|-
|50
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||3.7||64||2933||95.4||231.3||326.7||JB2
|-
|51
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|52
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|53
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|54
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|55
|AMD EPYC 7V12 64-Core Processor (16 CPUs) ||2.4||57||NA||63.5||277.6||341.1||CS2
|-
|56
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz (12 CPUs), ~3.7GHz
|3.7
|32
|2666
|91.45
|259.63
|351.1
|SS1
|-
|57
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|58
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|59
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|60
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|61
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||3.8||64||3200||87.8||294.2||382||HDR-A2000
|-
|62
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|63
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|64
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|65
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|66
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|67
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|68
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|69
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz ||3.0||4||986||84.0||351.4||435.4||TJS
|-
|70
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||3.5||16||1333||125.9||320.5||446.4||EFC
|-
|71
|Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz||1.9||16||1800||100.1||347.0||447.1||SM1
|-
|72
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|73
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||4.1||16||4104||62.7||386.6||449.3||GZH
|-
|74
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||2.4||128||2394||130.5||331.5||462||VDI
|-
|75
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|76
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|77
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|78
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|79
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|80
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|81
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|}

=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | #
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|1
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|2.5
|9.5
|12.0
|JW3
|-
|2
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz ||NVIDIA GeForce RTX 4090||24||16384||2.4||11.6||14.0||JW1
|-
|3
|AMD Ryzen 9 5950X 16-Core Processor (32 CPUs), ~3.4GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|3.3
|10.9
|14.2
|KW4
|-
|4
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4090||24||16384||2.5||12.1||14.6||JB1
|-
|5
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz ||NVIDIA GeForce RTX 4090||24||16384||2.7||12.7||15.4||TA1
|-
|6
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||2.9||12.7||15.6||PSM
|-
|7
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||3.1||13.3||16.4||CR4
|-
|8
|Intel(R) Core(TM) i9-14900K (32 CPUs) @ ~3.2GHz||NVIDIA GeForce RTX 4080 Super||16||10240||2.8||14.5||17.3||DF2
|-
|9
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||TM1
|-
|10
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||JM3
|-
|11
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||NVIDIA GeForce RTX 4080||16||9728||3.2||15.0||18.2||AW1
|-
|12
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|3.2
|15.0
|18.2
|TM2
|-
|13
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs)@ ~4.2GHz||NVIDIA GeForce RTX 4080 SUPER||16||10240||3.2||15.7||18.9||LC1
|-
|14
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||NVIDIA RTX 6000 Ada Generation||48||18176||4.9||14.2||19.1||JG3
|-
|15
|Intel(R) Xeon(R) CPU E5-2697 v4 @ 2.30GHz (72 CPUs)||NVIDIA GeForce RTX 4090||24||16384||3.9||16.2||20.1||CS3
|-
|16
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|17
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||3.4||19.2||22.6||HP1
|-
|18
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|3.9
|19.07
|22.97
|PW1
|-
|19
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||3.8||19.2||23.0||RS1
|-
|20
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||3.7||19.4||23.1||DD2
|-
|21
|12th Gen Intel(R) Core(TM) i7-12700F
|NVIDIA GeForce RTX 3080 Ti
|12
|10240
|3.75
|19.57
|23.32
|AUBNE1PC6602
|-
|22
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||4.0||19.6||23.6||GP1
|-
|23
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA A40||48||10752||3.5||20.3||23.8||Lenovo SR650 V2
|-
|24
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.1||20.1||24.2||JMM
|-
|25
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|26
|Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz (36 CPUs), ~3.0GHz
|NVIDIA RTX A6000
|48
|10752
|4.7
|22.7
|27.4
|CB2
|-
|27
|13th Gen Intel(R) Core(TM) i5-13600HX (20 CPUs) @ ~2.6GHz ||NVIDIA GeForce RTX 4090 Laptop GPU||16||9728||5.3||22.2||27.5||LC2
|-
|28
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz ||NVIDIA GeForce RTX 3080||10||8704||4.3||23.7||28||RB1
|-
|29
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|30
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs) ||NVIDIA RTX A5000||24||8192||4.4||24.1||28.5||SB1
|-
|31
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|32
|Intel(R) Xeon(R) W-2223 CPU @ 3.60GHz ||NVIDIA GeForce RTX 3090||24||10496||5.3||24.3||29.6||SSM2
|-
|33
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|34
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|35
|AMD Ryzen Threadripper 2920X 12-Core Processor (24 CPUs) @ 3.5GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.1||25.9||31||CR5
|-
|36
|Intel(R) Xeon(R) W-2145 CPU @ 3.70GHz (16 CPUs), ~3.7GHz
|NVIDIA RTX A5000
|24
|8192
|5.2
|26.3
|31.5
|CB3
|-
|37
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|38
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|39
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|40
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|41
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|42
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|43
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.9||28.4||34.3||HP2
|-
|44
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|45
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|46
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|47
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|48
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|49
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|50
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|51
|Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz ||NVIDIA GRID RTX8000P-48Q||48||4608||6.8||31.0||37.8||VJ1
|-
|52
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs) ||NVIDIA RTX A4000||16||6144||5.8||32.0||37.8||JS2
|-
|53
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|54
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||6.0||32.0||38.0||EFC
|-
|55
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|7.2
|31.4
|38.6
|YW1
|-
|56
|AMD EPYC 74F3 24-Core Processor (36 CPUs) @ 3.2GHz ||NVIDIA A10-24Q||24||8192||7.2||31.4||38.6||CS1
|-
|57
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||33.3||38.7||PA1
|-
|58
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce RTX 3060 Ti (LHR) ||8||4864||5.7||33.3||39.0||DF1
|-
|59
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||NVIDIA GeForce RTX 2060 SUPER (core 1647MHz, mem 1750MHz)||8||2176||5.6||33.4||39.1||GZH
|-
|60
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|61
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|62
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||35.1||41.6||JB2
|-
|63
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|64
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|65
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz ||NVIDIA RTX A4000||16||6144||7.2||35.3||42.5||CHR
|-
|66
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|67
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|68
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|69
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|70
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|71
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|72
|AMD Ryzen 7 7840HS ||NVIDIA GeForce RTX 4060 Laptop GPU||8||3072||7.0||40.6||47.6||CH1
|-
|73
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|74
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||NVIDIA GRID RTX6000P-4Q||24||4608||10.5||39.3||49.8||VDI
|-
|75
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|76
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA Tesla T4||16||2560||6.8||45.2||52.0||RJ1
|-
|77
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz
|NVIDIA GeForce RTX 3060
|8
|3584
|7.68
|45.92
|53.6
|AUBNEW1DQ54G3
|-
|78
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|79
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|80
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|81
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|82
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz (12 CPUs), ~3.7GHz
|NVIDIA GeForce GTX 1080
|8
|2560
|9.13
|52.13
|61.3
|SS1
|-
|83
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|84
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||NVIDIA RTX A2000||6||3328||9.1||54.4||63.5||HDR-A2000
|-
|85
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|86
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz ||NVIDIA GeForce RTX 4050 Laptop GPU||6||2560||7.9||59.4||67.3||AL1
|-
|87
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs), ~3.0GHz
|NVIDIA GeForce RTX A2000
|12
|3328
|8.6
|59.5
|68.1
|RA1
|-
|88
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|89
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA GeForce GTX 1660||6||1408||9.4||61.0||70.4||JFP
|-
|90
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|91
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce GTX 1650 SUPER||4||1280||10.3||64.6||74.9||TS1
|-
|92
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|93
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|94
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|NVIDIA RTX A2000 Laptop GPU
|4
|2560
|11.4
|72.6
|84.0
|SK1
|-
|95
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|96
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|97
|Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz (64 CPUs), ~2.3GHz||NVIDIA Tesla M60||16||4096||12||82.7||94.7||SM2
|-
|98
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|99
|Intel(R) Core(TM) i3-8100 CPU @ 3.60GHz ||NVIDIA Tesla K40c||12||2880||12.3||83.1||95.3||NWE
|-
|100
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA Quadro T2000||4||1024||13.3||85.2||95.5||SSM
|-
|101
|Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz (Laptop) ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|102
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs), ~3.8GHz
|NVIDIA Quadro T1000
|4
|896
|13.6
|91.3
|104.9
|CB4
|-
|103
|13th Gen Intel(R) Core(TM) i7-13800H (20 CPUs), ~2.5GHz
|NVIDIA RTX A500 Laptop GPU
|4
|2048
|14.8
|93.9
|108.7
|YB1
|-
|104
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|105
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|106
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|107
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|108
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|109
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|110
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|111
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|112
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|113
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|114
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz||NVIDIA T500||4||986||29.3||197.5||226.8||TJS
|-
|115
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|116
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|117
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||NVIDIA A10-4Q||24||8192||44.5||212.6||257.1||RBR
|-
|118
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|119
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|120
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|121
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|}

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | #
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|1

|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|73.3
|568.7
|642.0
|JW3
|-
|2
|AMD Ryzen 9 5950X 16-Core Processor (32 CPUs), ~3.4GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|75.2
|569.5
|644.7
|KW4
|-
|3
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||NVIDIA GeForce RTX 4090||24||16384||103.3||811.2||914.5||JB1
|-
|4
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz||NVIDIA GeForce RTX 4090||24||16384||105.9||813.7||919.6||TA1
|-
|5
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz||NVIDIA GeForce RTX 4090||24||16384||103.7||824.3||928.0||JW1
|-
|6
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||108.2||821.5||929.7||PSM
|-
|7
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||106.8||875.5||982.3||CR4
|-
|8
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz ||NVIDIA RTX 6000 Ada Generation||48||18176||119.6||877.8||997.4||JG3
|-
|9
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|134.0
|1049.9
|1183.9
|TM2
|-
|10
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||139.5||1115.1||1254.6||JM3
|-
|11
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|12
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||155.5||1176.2||1331.7||JMM
|-
|13
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|14
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|15
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||159.7||1216.2||1375.9||GP1
|-
|16
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||160.9||1240||1400.9||DD2
|-
|17
|Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz ||NVIDIA GeForce RTX 3090||24||10496||172.9||1249.8||1422.7||SIP
|-
|18
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|172.5
|1343.4
|15.15.9
|PW1
|-
|19
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|20
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||190.2||1493.2||1683.4||HP1
|-
|21
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|22
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|23
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|24
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|25
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||215.9||1678.7||1894.6||PA2
|-
|26
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||255.4||1691.7||1947.1||HP2
|-
|27
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|242.3
|1856.5
|2098.8
|YW1
|-
|28
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|29
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|30
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|31
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|32
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|33
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|34
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|35
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|36
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|37
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|38
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|39
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|40
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking | Back to TUFLOW Benchmarking]]
}}

Hardware Benchmarking - Results

2026-03-23T03:14:00Z

Abrar.Alttahir: /* GPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"

! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | #
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|1
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||128||5200||32.7||108.6||141.3||JB1
|-
|2
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||64||5600||36.7||114.2||150.9||TM1
|-
|3
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs), ~3.0GHz
|3
|32
|4800
|41.9
|122.4
|164.3
|RA1
|-
|4
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||2.0||128||4000||41.2||124.1||165.3||AW1
|-
|5
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs)||3.2||128||4800||38.5||127.0||165.5||SB1
|-
|6
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz||4.5||64||4800||33.4||136.0||169.4||PSM
|-
|7
|12th Gen Intel(R) Core(TM) i9-12900K||3.2||128||3200||40.7||129.9||170.6||DD2
|-
|8
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz||2.1||64||4800||41||137.2||178.2||RB1
|-
|9
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs) @ ~4.2GHz||4.2||32||4800||35.5||162.8||198.3||LC1
|-
|10
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs)||2.5||16||3200||48.4||170.5||218.9||TS1
|-
|11
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz||3.2||64||4800||53.3||169.1||222.4||CHR
|-
|12
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|13
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|14
|AMD Ryzen 5 7600X 6-Core Processor||4.7||32||4800||39.0||175.7||214.7||HP1
|-
|15
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|16
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|17
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|18
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|19
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||3.6||64||2133||62.7||191.1||253.8||PA1
|-
|20
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|21
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|22
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|23
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|3.2
|450
|
|50.0
|211.6
|261.6
|YW1
|-
|24
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|25
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|26
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||3.2||56||N/A||49.1||218.9||268||RBR
|-
|27
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|28
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|29
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|30
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|31
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|32
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|33
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|34
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||32||2113||68.2||217.9||286.1||HP2
|-
|35
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|36
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|37
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|38
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|39
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|40
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||3.5||128||2666||66.3||234.7||301||JG3
|-
|41
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|42
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|43
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|44
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|45
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|2.6
|32
|3200
|66.6
|243.3
|309.9
|SK1
|-
|46
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|47
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|48
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|49
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz||1.4||16||7467||173.8||152.8||326.6||AL1
|-
|50
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||3.7||64||2933||95.4||231.3||326.7||JB2
|-
|51
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|52
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|53
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|54
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|55
|AMD EPYC 7V12 64-Core Processor (16 CPUs) ||2.4||57||NA||63.5||277.6||341.1||CS2
|-
|56
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz (12 CPUs), ~3.7GHz
|3.7
|32
|2666
|91.45
|259.63
|351.1
|SS1
|-
|57
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|58
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|59
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|60
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|61
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||3.8||64||3200||87.8||294.2||382||HDR-A2000
|-
|62
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|63
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|64
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|65
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|66
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|67
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|68
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|69
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz ||3.0||4||986||84.0||351.4||435.4||TJS
|-
|70
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||3.5||16||1333||125.9||320.5||446.4||EFC
|-
|71
|Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz||1.9||16||1800||100.1||347.0||447.1||SM1
|-
|72
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|73
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||4.1||16||4104||62.7||386.6||449.3||GZH
|-
|74
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||2.4||128||2394||130.5||331.5||462||VDI
|-
|75
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|76
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|77
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|78
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|79
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|80
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|81
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|}

=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | #
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|1
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|2.5
|9.5
|12.0
|JW3
|-
|2
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz ||NVIDIA GeForce RTX 4090||24||16384||2.4||11.6||14.0||JW1
|-
|3
|AMD Ryzen 9 5950X 16-Core Processor (32 CPUs), ~3.4GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|3.3
|10.9
|14.2
|KW4
|-
|4
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4090||24||16384||2.5||12.1||14.6||JB1
|-
|5
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz ||NVIDIA GeForce RTX 4090||24||16384||2.7||12.7||15.4||TA1
|-
|6
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||2.9||12.7||15.6||PSM
|-
|7
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||3.1||13.3||16.4||CR4
|-
|8
|Intel(R) Core(TM) i9-14900K (32 CPUs) @ ~3.2GHz||NVIDIA GeForce RTX 4080 Super||16||10240||2.8||14.5||17.3||DF2
|-
|9
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||TM1
|-
|10
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||JM3
|-
|11
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||NVIDIA GeForce RTX 4080||16||9728||3.2||15.0||18.2||AW1
|-
|12
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|3.2
|15.0
|18.2
|TM2
|-
|13
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs)@ ~4.2GHz||NVIDIA GeForce RTX 4080 SUPER||16||10240||3.2||15.7||18.9||LC1
|-
|14
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||NVIDIA RTX 6000 Ada Generation||48||18176||4.9||14.2||19.1||JG3
|-
|15
|Intel(R) Xeon(R) CPU E5-2697 v4 @ 2.30GHz (72 CPUs)||NVIDIA GeForce RTX 4090||24||16384||3.9||16.2||20.1||CS3
|-
|16
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|17
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||3.4||19.2||22.6||HP1
|-
|18
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|3.9
|19.07
|22.97
|PW1
|-
|19
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||3.8||19.2||23.0||RS1
|-
|20
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||3.7||19.4||23.1||DD2
|-
|21
|12th Gen Intel(R) Core(TM) i7-12700F
|NVIDIA GeForce RTX 3080 Ti
|12
|10240
|3.75
|19.57
|23.32
|AUBNE1PC6602
|-
|22
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||4.0||19.6||23.6||GP1
|-
|23
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA A40||48||10752||3.5||20.3||23.8||Lenovo SR650 V2
|-
|24
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.1||20.1||24.2||JMM
|-
|25
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|26
|Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz (36 CPUs), ~3.0GHz
|NVIDIA RTX A6000
|48
|10752
|4.7
|22.7
|27.4
|CB2
|-
|27
|13th Gen Intel(R) Core(TM) i5-13600HX (20 CPUs) @ ~2.6GHz ||NVIDIA GeForce RTX 4090 Laptop GPU||16||9728||5.3||22.2||27.5||LC2
|-
|28
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz ||NVIDIA GeForce RTX 3080||10||8704||4.3||23.7||28||RB1
|-
|29
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|30
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs) ||NVIDIA RTX A5000||24||8192||4.4||24.1||28.5||SB1
|-
|31
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|32
|Intel(R) Xeon(R) W-2223 CPU @ 3.60GHz ||NVIDIA GeForce RTX 3090||24||10496||5.3||24.3||29.6||SSM2
|-
|33
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|34
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|35
|AMD Ryzen Threadripper 2920X 12-Core Processor (24 CPUs) @ 3.5GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.1||25.9||31||CR5
|-
|36
|Intel(R) Xeon(R) W-2145 CPU @ 3.70GHz (16 CPUs), ~3.7GHz
|NVIDIA RTX A5000
|24
|8192
|5.2
|26.3
|31.5
|CB3
|-
|37
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|38
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|39
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|40
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|41
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|42
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|43
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.9||28.4||34.3||HP2
|-
|44
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|45
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|46
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|47
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|48
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|49
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|50
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|51
|Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz ||NVIDIA GRID RTX8000P-48Q||48||4608||6.8||31.0||37.8||VJ1
|-
|52
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs) ||NVIDIA RTX A4000||16||6144||5.8||32.0||37.8||JS2
|-
|53
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|54
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||6.0||32.0||38.0||EFC
|-
|55
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|7.2
|31.4
|38.6
|YW1
|-
|56
|AMD EPYC 74F3 24-Core Processor (36 CPUs) @ 3.2GHz ||NVIDIA A10-24Q||24||8192||7.2||31.4||38.6||CS1
|-
|57
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||33.3||38.7||PA1
|-
|58
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce RTX 3060 Ti (LHR) ||8||4864||5.7||33.3||39.0||DF1
|-
|59
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||NVIDIA GeForce RTX 2060 SUPER (core 1647MHz, mem 1750MHz)||8||2176||5.6||33.4||39.1||GZH
|-
|60
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|61
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|62
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||35.1||41.6||JB2
|-
|63
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|64
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|65
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz ||NVIDIA RTX A4000||16||6144||7.2||35.3||42.5||CHR
|-
|66
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|67
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|68
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|69
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|70
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|71
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|72
|AMD Ryzen 7 7840HS ||NVIDIA GeForce RTX 4060 Laptop GPU||8||3072||7.0||40.6||47.6||CH1
|-
|73
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|74
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||NVIDIA GRID RTX6000P-4Q||24||4608||10.5||39.3||49.8||VDI
|-
|75
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|76
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA Tesla T4||16||2560||6.8||45.2||52.0||RJ1
|-
|77
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz
|NVIDIA GeForce RTX 3060
|8
|3584
|7.68
|45.92
|53.6
|AUBNEW1DQ54G3
|-
|78
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|79
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|80
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|81
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|82
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz (12 CPUs), ~3.7GHz
|NVIDIA GeForce GTX 1080
|8
|2560
|9.13
|52.13
|61.3
|SS1
|-
|83
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|84
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||NVIDIA RTX A2000||6||3328||9.1||54.4||63.5||HDR-A2000
|-
|85
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|86
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz ||NVIDIA GeForce RTX 4050 Laptop GPU||6||2560||7.9||59.4||67.3||AL1
|-
|87
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs), ~3.0GHz
|NVIDIA GeForce RTX A2000
|12
|3328
|8.6
|59.5
|68.1
|RA1
|-
|88
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|89
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA GeForce GTX 1660||6||1408||9.4||61.0||70.4||JFP
|-
|90
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|91
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce GTX 1650 SUPER||4||1280||10.3||64.6||74.9||TS1
|-
|92
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|93
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|94
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|NVIDIA RTX A2000 Laptop GPU
|4
|2560
|11.4
|72.6
|84.0
|SK1
|-
|95
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|96
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|97
|Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz (64 CPUs), ~2.3GHz||NVIDIA Tesla M60||16||4096||12||82.7||94.7||SM2
|-
|98
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|99
|Intel(R) Core(TM) i3-8100 CPU @ 3.60GHz ||NVIDIA Tesla K40c||12||2880||12.3||83.1||95.3||NWE
|-
|100
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA Quadro T2000||4||1024||13.3||85.2||95.5||SSM
|-
|101
|Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz (Laptop) ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|102
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs), ~3.8GHz
|NVIDIA Quadro T1000
|4
|896
|13.6
|91.3
|104.9
|CB4
|-
|103
|13th Gen Intel(R) Core(TM) i7-13800H (20 CPUs), ~2.5GHz
|NVIDIA RTX A500 Laptop GPU
|4
|2048
|14.8
|93.9
|108.7
|YB1
|-
|104
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|105
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|106
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|107
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|108
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|109
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|110
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|111
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|112
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|113
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|114
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz||NVIDIA T500||4||986||29.3||197.5||226.8||TJS
|-
|115
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|116
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|117
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||NVIDIA A10-4Q||24||8192||44.5||212.6||257.1||RBR
|-
|118
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|119
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|120
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|121
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|}

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | #
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|1

|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|73.3
|568.7
|642.0
|JW3
|-
|2
|AMD Ryzen 9 5950X 16-Core Processor (32 CPUs), ~3.4GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|75.2
|569.5
|644.7
|KW4
|-
|2
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||NVIDIA GeForce RTX 4090||24||16384||103.3||811.2||914.5||JB1
|-
|3
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz||NVIDIA GeForce RTX 4090||24||16384||105.9||813.7||919.6||TA1
|-
|4
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz||NVIDIA GeForce RTX 4090||24||16384||103.7||824.3||928.0||JW1
|-
|5
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||108.2||821.5||929.7||PSM
|-
|6
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||106.8||875.5||982.3||CR4
|-
|7
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz ||NVIDIA RTX 6000 Ada Generation||48||18176||119.6||877.8||997.4||JG3
|-
|8
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|134.0
|1049.9
|1183.9
|TM2
|-
|9
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||139.5||1115.1||1254.6||JM3
|-
|10
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|11
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||155.5||1176.2||1331.7||JMM
|-
|12
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|13
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|14
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||159.7||1216.2||1375.9||GP1
|-
|15
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||160.9||1240||1400.9||DD2
|-
|16
|Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz ||NVIDIA GeForce RTX 3090||24||10496||172.9||1249.8||1422.7||SIP
|-
|17
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|172.5
|1343.4
|15.15.9
|PW1
|-
|18
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|19
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||190.2||1493.2||1683.4||HP1
|-
|20
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|21
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|22
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|23
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|24
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||215.9||1678.7||1894.6||PA2
|-
|25
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||255.4||1691.7||1947.1||HP2
|-
|26
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|242.3
|1856.5
|2098.8
|YW1
|-
|27
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|28
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|29
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|30
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|31
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|32
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|33
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|34
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|35
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|36
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|37
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|38
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|39
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking | Back to TUFLOW Benchmarking]]
}}

Hardware Benchmarking - Results

2026-03-23T02:22:22Z

Abrar.Alttahir: /* GPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"

! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | #
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|1
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||128||5200||32.7||108.6||141.3||JB1
|-
|2
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||64||5600||36.7||114.2||150.9||TM1
|-
|3
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs), ~3.0GHz
|3
|32
|4800
|41.9
|122.4
|164.3
|RA1
|-
|4
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||2.0||128||4000||41.2||124.1||165.3||AW1
|-
|5
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs)||3.2||128||4800||38.5||127.0||165.5||SB1
|-
|6
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz||4.5||64||4800||33.4||136.0||169.4||PSM
|-
|7
|12th Gen Intel(R) Core(TM) i9-12900K||3.2||128||3200||40.7||129.9||170.6||DD2
|-
|8
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz||2.1||64||4800||41||137.2||178.2||RB1
|-
|9
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs) @ ~4.2GHz||4.2||32||4800||35.5||162.8||198.3||LC1
|-
|10
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs)||2.5||16||3200||48.4||170.5||218.9||TS1
|-
|11
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz||3.2||64||4800||53.3||169.1||222.4||CHR
|-
|12
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|13
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|14
|AMD Ryzen 5 7600X 6-Core Processor||4.7||32||4800||39.0||175.7||214.7||HP1
|-
|15
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|16
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|17
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|18
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|19
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||3.6||64||2133||62.7||191.1||253.8||PA1
|-
|20
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|21
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|22
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|23
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|3.2
|450
|
|50.0
|211.6
|261.6
|YW1
|-
|24
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|25
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|26
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||3.2||56||N/A||49.1||218.9||268||RBR
|-
|27
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|28
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|29
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|30
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|31
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|32
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|33
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|34
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||32||2113||68.2||217.9||286.1||HP2
|-
|35
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|36
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|37
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|38
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|39
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|40
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||3.5||128||2666||66.3||234.7||301||JG3
|-
|41
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|42
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|43
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|44
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|45
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|2.6
|32
|3200
|66.6
|243.3
|309.9
|SK1
|-
|46
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|47
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|48
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|49
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz||1.4||16||7467||173.8||152.8||326.6||AL1
|-
|50
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||3.7||64||2933||95.4||231.3||326.7||JB2
|-
|51
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|52
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|53
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|54
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|55
|AMD EPYC 7V12 64-Core Processor (16 CPUs) ||2.4||57||NA||63.5||277.6||341.1||CS2
|-
|56
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz (12 CPUs), ~3.7GHz
|3.7
|32
|2666
|91.45
|259.63
|351.1
|SS1
|-
|57
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|58
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|59
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|60
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|61
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||3.8||64||3200||87.8||294.2||382||HDR-A2000
|-
|62
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|63
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|64
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|65
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|66
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|67
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|68
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|69
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz ||3.0||4||986||84.0||351.4||435.4||TJS
|-
|70
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||3.5||16||1333||125.9||320.5||446.4||EFC
|-
|71
|Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz||1.9||16||1800||100.1||347.0||447.1||SM1
|-
|72
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|73
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||4.1||16||4104||62.7||386.6||449.3||GZH
|-
|74
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||2.4||128||2394||130.5||331.5||462||VDI
|-
|75
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|76
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|77
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|78
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|79
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|80
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|81
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|}

=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | #
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|1
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|2.5
|9.5
|12.0
|JW3
|-
|2
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz ||NVIDIA GeForce RTX 4090||24||16384||2.4||11.6||14.0||JW1
|-
|2
|AMD Ryzen 9 5950X 16-Core Processor (32 CPUs), ~3.4GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|3.3
|10.9
|14.2
|KW4
|-
|3
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4090||24||16384||2.5||12.1||14.6||JB1
|-
|4
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz ||NVIDIA GeForce RTX 4090||24||16384||2.7||12.7||15.4||TA1
|-
|5
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||2.9||12.7||15.6||PSM
|-
|6
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||3.1||13.3||16.4||CR4
|-
|7
|Intel(R) Core(TM) i9-14900K (32 CPUs) @ ~3.2GHz||NVIDIA GeForce RTX 4080 Super||16||10240||2.8||14.5||17.3||DF2
|-
|8
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||TM1
|-
|9
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||JM3
|-
|10
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||NVIDIA GeForce RTX 4080||16||9728||3.2||15.0||18.2||AW1
|-
|11
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|3.2
|15.0
|18.2
|TM2
|-
|12
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs)@ ~4.2GHz||NVIDIA GeForce RTX 4080 SUPER||16||10240||3.2||15.7||18.9||LC1
|-
|13
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||NVIDIA RTX 6000 Ada Generation||48||18176||4.9||14.2||19.1||JG3
|-
|14
|Intel(R) Xeon(R) CPU E5-2697 v4 @ 2.30GHz (72 CPUs)||NVIDIA GeForce RTX 4090||24||16384||3.9||16.2||20.1||CS3
|-
|15
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|16
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||3.4||19.2||22.6||HP1
|-
|17
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|3.9
|19.07
|22.97
|PW1
|-
|18
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||3.8||19.2||23.0||RS1
|-
|19
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||3.7||19.4||23.1||DD2
|-
|20
|12th Gen Intel(R) Core(TM) i7-12700F
|NVIDIA GeForce RTX 3080 Ti
|12
|10240
|3.75
|19.57
|23.32
|AUBNE1PC6602
|-
|21
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||4.0||19.6||23.6||GP1
|-
|22
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA A40||48||10752||3.5||20.3||23.8||Lenovo SR650 V2
|-
|23
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.1||20.1||24.2||JMM
|-
|24
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|25
|Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz (36 CPUs), ~3.0GHz
|NVIDIA RTX A6000
|48
|10752
|4.7
|22.7
|27.4
|CB2
|-
|26
|13th Gen Intel(R) Core(TM) i5-13600HX (20 CPUs) @ ~2.6GHz ||NVIDIA GeForce RTX 4090 Laptop GPU||16||9728||5.3||22.2||27.5||LC2
|-
|27
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz ||NVIDIA GeForce RTX 3080||10||8704||4.3||23.7||28||RB1
|-
|28
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|29
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs) ||NVIDIA RTX A5000||24||8192||4.4||24.1||28.5||SB1
|-
|30
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|31
|Intel(R) Xeon(R) W-2223 CPU @ 3.60GHz ||NVIDIA GeForce RTX 3090||24||10496||5.3||24.3||29.6||SSM2
|-
|32
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|33
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|34
|AMD Ryzen Threadripper 2920X 12-Core Processor (24 CPUs) @ 3.5GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.1||25.9||31||CR5
|-
|35
|Intel(R) Xeon(R) W-2145 CPU @ 3.70GHz (16 CPUs), ~3.7GHz
|NVIDIA RTX A5000
|24
|8192
|5.2
|26.3
|31.5
|CB3
|-
|36
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|37
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|38
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|39
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|40
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|41
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|42
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.9||28.4||34.3||HP2
|-
|43
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|44
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|45
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|46
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|47
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|48
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|49
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|50
|Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz ||NVIDIA GRID RTX8000P-48Q||48||4608||6.8||31.0||37.8||VJ1
|-
|51
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs) ||NVIDIA RTX A4000||16||6144||5.8||32.0||37.8||JS2
|-
|52
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|53
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||6.0||32.0||38.0||EFC
|-
|54
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|7.2
|31.4
|38.6
|YW1
|-
|55
|AMD EPYC 74F3 24-Core Processor (36 CPUs) @ 3.2GHz ||NVIDIA A10-24Q||24||8192||7.2||31.4||38.6||CS1
|-
|56
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||33.3||38.7||PA1
|-
|57
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce RTX 3060 Ti (LHR) ||8||4864||5.7||33.3||39.0||DF1
|-
|58
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||NVIDIA GeForce RTX 2060 SUPER (core 1647MHz, mem 1750MHz)||8||2176||5.6||33.4||39.1||GZH
|-
|59
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|60
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|61
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||35.1||41.6||JB2
|-
|62
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|63
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|64
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz ||NVIDIA RTX A4000||16||6144||7.2||35.3||42.5||CHR
|-
|65
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|66
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|67
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|68
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|69
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|70
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|71
|AMD Ryzen 7 7840HS ||NVIDIA GeForce RTX 4060 Laptop GPU||8||3072||7.0||40.6||47.6||CH1
|-
|72
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|73
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||NVIDIA GRID RTX6000P-4Q||24||4608||10.5||39.3||49.8||VDI
|-
|74
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|75
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA Tesla T4||16||2560||6.8||45.2||52.0||RJ1
|-
|76
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz
|NVIDIA GeForce RTX 3060
|8
|3584
|7.68
|45.92
|53.6
|AUBNEW1DQ54G3
|-
|77
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|78
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|79
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|80
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|81
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz (12 CPUs), ~3.7GHz
|NVIDIA GeForce GTX 1080
|8
|2560
|9.13
|52.13
|61.3
|SS1
|-
|82
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|83
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||NVIDIA RTX A2000||6||3328||9.1||54.4||63.5||HDR-A2000
|-
|84
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|85
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz ||NVIDIA GeForce RTX 4050 Laptop GPU||6||2560||7.9||59.4||67.3||AL1
|-
|86
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs), ~3.0GHz
|NVIDIA GeForce RTX A2000
|12
|3328
|8.6
|59.5
|68.1
|RA1
|-
|87
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|88
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA GeForce GTX 1660||6||1408||9.4||61.0||70.4||JFP
|-
|89
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|90
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce GTX 1650 SUPER||4||1280||10.3||64.6||74.9||TS1
|-
|91
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|92
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|93
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|NVIDIA RTX A2000 Laptop GPU
|4
|2560
|11.4
|72.6
|84.0
|SK1
|-
|94
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|95
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|96
|Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz (64 CPUs), ~2.3GHz||NVIDIA Tesla M60||16||4096||12||82.7||94.7||SM2
|-
|97
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|98
|Intel(R) Core(TM) i3-8100 CPU @ 3.60GHz ||NVIDIA Tesla K40c||12||2880||12.3||83.1||95.3||NWE
|-
|99
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA Quadro T2000||4||1024||13.3||85.2||95.5||SSM
|-
|100
|Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz (Laptop) ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|101
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs), ~3.8GHz
|NVIDIA Quadro T1000
|4
|896
|13.6
|91.3
|104.9
|CB4
|-
|102
|13th Gen Intel(R) Core(TM) i7-13800H (20 CPUs), ~2.5GHz
|NVIDIA RTX A500 Laptop GPU
|4
|2048
|14.8
|93.9
|108.7
|YB1
|-
|103
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|104
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|105
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|106
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|107
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|108
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|109
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|110
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|111
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|112
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|113
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz||NVIDIA T500||4||986||29.3||197.5||226.8||TJS
|-
|114
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|115
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|116
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||NVIDIA A10-4Q||24||8192||44.5||212.6||257.1||RBR
|-
|117
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|118
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|119
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|120
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|}

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | #
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|1

|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|73.3
|568.7
|642.0
|JW3
|-
|2
|AMD Ryzen 9 5950X 16-Core Processor (32 CPUs), ~3.4GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|75.2
|569.5
|644.7
|KW4
|-
|2
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||NVIDIA GeForce RTX 4090||24||16384||103.3||811.2||914.5||JB1
|-
|3
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz||NVIDIA GeForce RTX 4090||24||16384||105.9||813.7||919.6||TA1
|-
|4
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz||NVIDIA GeForce RTX 4090||24||16384||103.7||824.3||928.0||JW1
|-
|5
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||108.2||821.5||929.7||PSM
|-
|6
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||106.8||875.5||982.3||CR4
|-
|7
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz ||NVIDIA RTX 6000 Ada Generation||48||18176||119.6||877.8||997.4||JG3
|-
|8
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|134.0
|1049.9
|1183.9
|TM2
|-
|9
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||139.5||1115.1||1254.6||JM3
|-
|10
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|11
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||155.5||1176.2||1331.7||JMM
|-
|12
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|13
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|14
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||159.7||1216.2||1375.9||GP1
|-
|15
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||160.9||1240||1400.9||DD2
|-
|16
|Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz ||NVIDIA GeForce RTX 3090||24||10496||172.9||1249.8||1422.7||SIP
|-
|17
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|172.5
|1343.4
|15.15.9
|PW1
|-
|18
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|19
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||190.2||1493.2||1683.4||HP1
|-
|20
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|21
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|22
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|23
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|24
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||215.9||1678.7||1894.6||PA2
|-
|25
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||255.4||1691.7||1947.1||HP2
|-
|26
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|242.3
|1856.5
|2098.8
|YW1
|-
|27
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|28
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|29
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|30
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|31
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|32
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|33
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|34
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|35
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|36
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|37
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|38
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|39
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking | Back to TUFLOW Benchmarking]]
}}

Convert to TS1

2026-03-18T02:06:01Z

Abrar.Alttahir: /* Options */

=Introduction=
'''convert_to_ts1.exe''' converts output from hydrologic models to the .ts1 format recognised by TUFLOW. The .ts1 format is a .csv file that includes indexing and header information, significantly reducing the time required for TUFLOW to read inflow hydrographs. If numerous inflow hydrographs are present, use of this format is strongly recommended.

Any number of input files (of the same format) can be specified, and wildcards (e.g. “*.out”) may be used to select a group of files. The available options are described in the table below. One input format switch and one output format switch should be specified. However, the output format defaults to ts1, so this flag may be omitted.

For most options, an additional file “_peak_Q.csv” is produced, summarising the peak flows for each hydrograph. If a group of files is specified, the _peak_Q.csv file summarises all files within the group, and a second file “_peak_F.csv” identifies which file produced the peak flow across all files. This is useful for determining which storm duration produced the peak flow (i.e. the critical duration event).

The program currently supports the hydrology models most commonly used within Australia. Additional formats can be incorporated by supplying example files/formats and relevant information to [mailto:support@tuflow.com support@tuflow.com].

The .ts1 file format is outlined on the following page: [[TS1 File Format]].

=Options=
'''TABLE 1: convert_to_ts1 Options (Switches)'''
{| align="center" class="wikitable"

! Switch
! width=85% | Description
|-
!colspan="2" style="background-color:#005581; font-weight:bold; color:white;"| Input File Format Switches
|-
|"-rafts"|| Input files are XP-RAFTS .tot and .loc files.
|-
|"-rorb"|| Input files are RORB .out files. The dt option must also be specified.
|-
|"-rows"|| The inflow hydrographs are in space or comma delimited files with the data in blocks of one or more rows for each hydrograph. No time data exists in the input files.
|-
|"-urbs"|| Input files are URBS .q files, which must use relative time in hours from the start of the hydrograph.
|-
|"-wbnm"|| Input files are WBNM _Meta.out files. Only outputs in the .ts1 format. Two files are created:
*loc_.ts1 containing the local hydrographs.
*tot_.ts1 containing the total hydrographs.
|-
|"-xp"|| Input files are XP-SWMM .int or .ext files.
|-
!colspan="2" style="background-color:#005581; font-weight:bold; color:white;"| Output File Format Switches
|-
|"-csv"|| Output file(s) in .csv format.
|-
|"-ts1"|| Output file(s) in .ts1 format (the default).
|-
!colspan="2" style="background-color:#005581; font-weight:bold; color:white;"| RAFTS and WBNM Specific Switches
|-
|"-mw"|| Multiple WBNM Durations: Read multiple durations from a wbnm *.out file. Must be used in conjunction with the -wbnm switch. Event names are read directly from the *.out file. Please note: Multiple file processing using the * wildcard feature not yet supported with the -mw switch enabled.
|-
|-
|"-mr event_names.txt"|| Multiple Rafts Durations: Read multiple durations from a rafts *.loc or *.tot file. Must be used in conjunction with the -rafts switch. Events are read directly from the *.out file. Names of each event are extracted from the text file event_names.txt (refer example below). The order of the durations in event_names.txt has to be the same as the order of ticked durations in XPRAFTS. Please note: Routing increments must be the same for all storm durations. Multiple file processing using the * wildcard feature not yet supported with the -mr switch enabled.
|-
!colspan="2" style="background-color:#005581; font-weight:bold; color:white;"| Miscellaneous Switches
|-
|"-b"|| Batch mode. Suppresses prompt to press Enter at end of processing. Used in .bat files where two or more files are to be processed in succession.
|-
|<nowiki>"-dt<dt>"</nowiki>|| Time increment of RORB hydrographs in minutes. For example, 5 minutes would be specified as dt5.
|-
|"-s0"|| Insert a zero flow before the start of the hydrograph.
|-
|"-e0"|| Insert a zero flow after the end of the hydrograph.
|-
|"-thour"|| Output time in hours (for .csv output format only).
|}

=Examples=
<ol>
<li>Outputs two .ts1 files, one the local hydrographs and the other for the total hydrographs in Q100_Meta.out. 
<tt>convert_to_ts1.exe -wbnm -ts1 Q100_Meta.out</tt>
<li>Outputs .ts1 files for every .out file in the folder. A summary of the peak flows can be found in the _peak_Q.csv and _peak_F.csv files. 
<tt>convert_to_ts1.exe -rorb -ts1 -dt5 *.out</tt>
<li>Outputs .ts1 from .int file. 
<tt>convert_to_ts1.exe -xp -ts1 Q100.int</tt>
<li>Outputs .ts1 files for every Q100 .loc and .tot file in the folder. A summary of the peak flows can be found in the _peak_Q.csv and _peak_F.csv files. 
<tt>convert_to_ts1.exe -rafts -ts1 Q100*.loc Q100*.tot</tt>
<li>Outputs .csv files for the four files specified. The output time is in hours. 
<tt>convert_to_ts1.exe -rafts -csv -thour Q100.tot Q050.tot Q100.loc Q050.loc</tt>
<li>Outputs a separate .csv file for each duration specified within event_names.txt and the result my_results.tot. The order of the durations in event_names.txt is important and has to be the same as the order of ticked durations in XPRAFTS. Example is shown below. 
<tt>convert_to_ts1.exe -rafts -mr event_names.txt -e0 -csv my_results.tot</tt> 
 
[[File:RAFTS Storm Control.png | 500px]] 
 
<li>Outputs a separate .csv file for each event within the wbnm output file Q100.out. 
<tt>convert_to_ts1.exe -wbnm -mw -csv Q100.out</tt> 
 
</ol>

{{Tips Navigation
|uplink=[[TUFLOW_Utilities | Back to TUFLOW Utilities]]
}}

Convert to TS1

2026-03-18T02:02:27Z

Abrar.Alttahir: /* Introduction */

=Introduction=
'''convert_to_ts1.exe''' converts output from hydrologic models to the .ts1 format recognised by TUFLOW. The .ts1 format is a .csv file that includes indexing and header information, significantly reducing the time required for TUFLOW to read inflow hydrographs. If numerous inflow hydrographs are present, use of this format is strongly recommended.

Any number of input files (of the same format) can be specified, and wildcards (e.g. “*.out”) may be used to select a group of files. The available options are described in the table below. One input format switch and one output format switch should be specified. However, the output format defaults to ts1, so this flag may be omitted.

For most options, an additional file “_peak_Q.csv” is produced, summarising the peak flows for each hydrograph. If a group of files is specified, the _peak_Q.csv file summarises all files within the group, and a second file “_peak_F.csv” identifies which file produced the peak flow across all files. This is useful for determining which storm duration produced the peak flow (i.e. the critical duration event).

The program currently supports the hydrology models most commonly used within Australia. Additional formats can be incorporated by supplying example files/formats and relevant information to [mailto:support@tuflow.com support@tuflow.com].

The .ts1 file format is outlined on the following page: [[TS1 File Format]].

=Options=
'''TABLE 1: convert_to_ts1 Options (Switches)'''
{| align="center" class="wikitable"

! Switch
! width=85% | Description
|-
!colspan="2" style="background-color:#005581; font-weight:bold; color:white;"| Input File Format Switches
|-
|"-rafts"|| Input files are XP-RAFTS .tot and .loc files.
|-
|"-rorb"|| Input files are RORB .out files. The dt option must also be specified.
|-
|"-rows"|| The inflow hydrographs are in space or comma delimited files with the data in blocks of one or more rows for each hydrograph. No time data exists in the input files.
|-
|"-urbs"|| Input files are URBS .q files.
|-
|"-wbnm"|| Input files are WBNM _Meta.out files. 
Only outputs in the .ts1 format. Two files are created: 
*loc_.ts1 containing the local hydrographs.
*tot_.ts1 containing the total hydrographs.
|-
|"-xp"|| Input files are XP-SWMM .int or .ext files.
|-
!colspan="2" style="background-color:#005581; font-weight:bold; color:white;"| Output File Format Switches
|-
|"-csv"|| Output file(s) in .csv format.
|-
|"-ts1"|| Output file(s) in .ts1 format (the default).
|-
!colspan="2" style="background-color:#005581; font-weight:bold; color:white;"| RAFTS and WBNM Specific Switches
|-
|"-mw"|| Multiple WBNM Durations: Read multiple durations from a wbnm *.out file. Must be used in conjunction with the -wbnm switch. Event names are read directly from the *.out file. Please note: Multiple file processing using the * wildcard feature not yet supported with the -mw switch enabled.
|-
|-
|"-mr event_names.txt"|| Multiple Rafts Durations: Read multiple durations from a rafts *.loc or *.tot file. Must be used in conjunction with the -rafts switch. Events are read directly from the *.out file. Names of each event are extracted from the text file event_names.txt (refer example below). The order of the durations in event_names.txt has to be the same as the order of ticked durations in XPRAFTS. Please note: Routing increments must be the same for all storm durations. Multiple file processing using the * wildcard feature not yet supported with the -mr switch enabled.
|-
!colspan="2" style="background-color:#005581; font-weight:bold; color:white;"| Miscellaneous Switches
|-
|"-b"|| Batch mode. Suppresses prompt to press Enter at end of processing. Used in .bat files where two or more files are to be processed in succession.
|-
|<nowiki>"-dt<dt>"</nowiki>|| Time increment of RORB hydrographs in minutes. For example, 5 minutes would be specified as dt5.
|-
|"-s0"|| Insert a zero flow before the start of the hydrograph.
|-
|"-e0"|| Insert a zero flow after the end of the hydrograph.
|-
|"-thour"|| Output time in hours (for .csv output format only).
|}

=Examples=
<ol>
<li>Outputs two .ts1 files, one the local hydrographs and the other for the total hydrographs in Q100_Meta.out. 
<tt>convert_to_ts1.exe -wbnm -ts1 Q100_Meta.out</tt>
<li>Outputs .ts1 files for every .out file in the folder. A summary of the peak flows can be found in the _peak_Q.csv and _peak_F.csv files. 
<tt>convert_to_ts1.exe -rorb -ts1 -dt5 *.out</tt>
<li>Outputs .ts1 from .int file. 
<tt>convert_to_ts1.exe -xp -ts1 Q100.int</tt>
<li>Outputs .ts1 files for every Q100 .loc and .tot file in the folder. A summary of the peak flows can be found in the _peak_Q.csv and _peak_F.csv files. 
<tt>convert_to_ts1.exe -rafts -ts1 Q100*.loc Q100*.tot</tt>
<li>Outputs .csv files for the four files specified. The output time is in hours. 
<tt>convert_to_ts1.exe -rafts -csv -thour Q100.tot Q050.tot Q100.loc Q050.loc</tt>
<li>Outputs a separate .csv file for each duration specified within event_names.txt and the result my_results.tot. The order of the durations in event_names.txt is important and has to be the same as the order of ticked durations in XPRAFTS. Example is shown below. 
<tt>convert_to_ts1.exe -rafts -mr event_names.txt -e0 -csv my_results.tot</tt> 
 
[[File:RAFTS Storm Control.png | 500px]] 
 
<li>Outputs a separate .csv file for each event within the wbnm output file Q100.out. 
<tt>convert_to_ts1.exe -wbnm -mw -csv Q100.out</tt> 
 
</ol>

{{Tips Navigation
|uplink=[[TUFLOW_Utilities | Back to TUFLOW Utilities]]
}}

Hardware Benchmarking - Results

2026-02-28T05:24:47Z

Abrar.Alttahir: /* CPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"

! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | #
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|1
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||128||5200||32.7||108.6||141.3||JB1
|-
|2
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||64||5600||36.7||114.2||150.9||TM1
|-
|3
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs), ~3.0GHz
|3
|32
|4800
|41.9
|122.4
|164.3
|RA1
|-
|4
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||2.0||128||4000||41.2||124.1||165.3||AW1
|-
|5
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs)||3.2||128||4800||38.5||127.0||165.5||SB1
|-
|6
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz||4.5||64||4800||33.4||136.0||169.4||PSM
|-
|7
|12th Gen Intel(R) Core(TM) i9-12900K||3.2||128||3200||40.7||129.9||170.6||DD2
|-
|8
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz||2.1||64||4800||41||137.2||178.2||RB1
|-
|9
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs) @ ~4.2GHz||4.2||32||4800||35.5||162.8||198.3||LC1
|-
|10
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs)||2.5||16||3200||48.4||170.5||218.9||TS1
|-
|11
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz||3.2||64||4800||53.3||169.1||222.4||CHR
|-
|12
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|13
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|14
|AMD Ryzen 5 7600X 6-Core Processor||4.7||32||4800||39.0||175.7||214.7||HP1
|-
|15
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|16
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|17
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|18
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|19
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||3.6||64||2133||62.7||191.1||253.8||PA1
|-
|20
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|21
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|22
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|23
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|3.2
|450
|
|50.0
|211.6
|261.6
|YW1
|-
|24
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|25
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|26
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||3.2||56||N/A||49.1||218.9||268||RBR
|-
|27
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|28
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|29
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|30
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|31
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|32
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|33
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|34
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||32||2113||68.2||217.9||286.1||HP2
|-
|35
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|36
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|37
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|38
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|39
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|40
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||3.5||128||2666||66.3||234.7||301||JG3
|-
|41
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|42
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|43
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|44
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|45
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|2.6
|32
|3200
|66.6
|243.3
|309.9
|SK1
|-
|46
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|47
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|48
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|49
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz||1.4||16||7467||173.8||152.8||326.6||AL1
|-
|50
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||3.7||64||2933||95.4||231.3||326.7||JB2
|-
|51
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|52
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|53
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|54
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|55
|AMD EPYC 7V12 64-Core Processor (16 CPUs) ||2.4||57||NA||63.5||277.6||341.1||CS2
|-
|56
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz (12 CPUs), ~3.7GHz
|3.7
|32
|2666
|91.45
|259.63
|351.1
|SS1
|-
|57
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|58
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|59
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|60
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|61
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||3.8||64||3200||87.8||294.2||382||HDR-A2000
|-
|62
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|63
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|64
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|65
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|66
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|67
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|68
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|69
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz ||3.0||4||986||84.0||351.4||435.4||TJS
|-
|70
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||3.5||16||1333||125.9||320.5||446.4||EFC
|-
|71
|Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz||1.9||16||1800||100.1||347.0||447.1||SM1
|-
|72
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|73
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||4.1||16||4104||62.7||386.6||449.3||GZH
|-
|74
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||2.4||128||2394||130.5||331.5||462||VDI
|-
|75
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|76
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|77
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|78
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|79
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|80
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|81
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|}

=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | #
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|1
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|2.5
|9.5
|12.0
|JW3
|-
|2
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz ||NVIDIA GeForce RTX 4090||24||16384||2.4||11.6||14.0||JW1
|-
|3
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4090||24||16384||2.5||12.1||14.6||JB1
|-
|4
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz ||NVIDIA GeForce RTX 4090||24||16384||2.7||12.7||15.4||TA1
|-
|5
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||2.9||12.7||15.6||PSM
|-
|6
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||3.1||13.3||16.4||CR4
|-
|7
|Intel(R) Core(TM) i9-14900K (32 CPUs) @ ~3.2GHz||NVIDIA GeForce RTX 4080 Super||16||10240||2.8||14.5||17.3||DF2
|-
|8
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||TM1
|-
|9
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||JM3
|-
|10
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||NVIDIA GeForce RTX 4080||16||9728||3.2||15.0||18.2||AW1
|-
|11
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|3.2
|15.0
|18.2
|TM2
|-
|12
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs)@ ~4.2GHz||NVIDIA GeForce RTX 4080 SUPER||16||10240||3.2||15.7||18.9||LC1
|-
|13
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||NVIDIA RTX 6000 Ada Generation||48||18176||4.9||14.2||19.1||JG3
|-
|14
|Intel(R) Xeon(R) CPU E5-2697 v4 @ 2.30GHz (72 CPUs)||NVIDIA GeForce RTX 4090||24||16384||3.9||16.2||20.1||CS3
|-
|15
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|16
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||3.4||19.2||22.6||HP1
|-
|17
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|3.9
|19.07
|22.97
|PW1
|-
|18
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||3.8||19.2||23.0||RS1
|-
|19
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||3.7||19.4||23.1||DD2
|-
|20
|12th Gen Intel(R) Core(TM) i7-12700F
|NVIDIA GeForce RTX 3080 Ti
|12
|10240
|3.75
|19.57
|23.32
|AUBNE1PC6602
|-
|21
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||4.0||19.6||23.6||GP1
|-
|22
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA A40||48||10752||3.5||20.3||23.8||Lenovo SR650 V2
|-
|23
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.1||20.1||24.2||JMM
|-
|24
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|25
|Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz (36 CPUs), ~3.0GHz
|NVIDIA RTX A6000
|48
|10752
|4.7
|22.7
|27.4
|CB2
|-
|26
|13th Gen Intel(R) Core(TM) i5-13600HX (20 CPUs) @ ~2.6GHz ||NVIDIA GeForce RTX 4090 Laptop GPU||16||9728||5.3||22.2||27.5||LC2
|-
|27
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz ||NVIDIA GeForce RTX 3080||10||8704||4.3||23.7||28||RB1
|-
|28
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|29
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs) ||NVIDIA RTX A5000||24||8192||4.4||24.1||28.5||SB1
|-
|30
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|31
|Intel(R) Xeon(R) W-2223 CPU @ 3.60GHz ||NVIDIA GeForce RTX 3090||24||10496||5.3||24.3||29.6||SSM2
|-
|32
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|33
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|34
|AMD Ryzen Threadripper 2920X 12-Core Processor (24 CPUs) @ 3.5GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.1||25.9||31||CR5
|-
|35
|Intel(R) Xeon(R) W-2145 CPU @ 3.70GHz (16 CPUs), ~3.7GHz
|NVIDIA RTX A5000
|24
|8192
|5.2
|26.3
|31.5
|CB3
|-
|36
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|37
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|38
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|39
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|40
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|41
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|42
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.9||28.4||34.3||HP2
|-
|43
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|44
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|45
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|46
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|47
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|48
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|49
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|50
|Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz ||NVIDIA GRID RTX8000P-48Q||48||4608||6.8||31.0||37.8||VJ1
|-
|51
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs) ||NVIDIA RTX A4000||16||6144||5.8||32.0||37.8||JS2
|-
|52
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|53
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||6.0||32.0||38.0||EFC
|-
|54
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|7.2
|31.4
|38.6
|YW1
|-
|55
|AMD EPYC 74F3 24-Core Processor (36 CPUs) @ 3.2GHz ||NVIDIA A10-24Q||24||8192||7.2||31.4||38.6||CS1
|-
|56
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||33.3||38.7||PA1
|-
|57
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce RTX 3060 Ti (LHR) ||8||4864||5.7||33.3||39.0||DF1
|-
|58
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||NVIDIA GeForce RTX 2060 SUPER (core 1647MHz, mem 1750MHz)||8||2176||5.6||33.4||39.1||GZH
|-
|59
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|60
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|61
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||35.1||41.6||JB2
|-
|62
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|63
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|64
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz ||NVIDIA RTX A4000||16||6144||7.2||35.3||42.5||CHR
|-
|65
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|66
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|67
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|68
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|69
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|70
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|71
|AMD Ryzen 7 7840HS ||NVIDIA GeForce RTX 4060 Laptop GPU||8||3072||7.0||40.6||47.6||CH1
|-
|72
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|73
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||NVIDIA GRID RTX6000P-4Q||24||4608||10.5||39.3||49.8||VDI
|-
|74
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|75
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA Tesla T4||16||2560||6.8||45.2||52.0||RJ1
|-
|76
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz
|NVIDIA GeForce RTX 3060
|8
|3584
|7.68
|45.92
|53.6
|AUBNEW1DQ54G3
|-
|77
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|78
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|79
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|80
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|81
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz (12 CPUs), ~3.7GHz
|NVIDIA GeForce GTX 1080
|8
|2560
|9.13
|52.13
|61.3
|SS1
|-
|82
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|83
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||NVIDIA RTX A2000||6||3328||9.1||54.4||63.5||HDR-A2000
|-
|84
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|85
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz ||NVIDIA GeForce RTX 4050 Laptop GPU||6||2560||7.9||59.4||67.3||AL1
|-
|86
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs), ~3.0GHz
|NVIDIA GeForce RTX A2000
|12
|3328
|8.6
|59.5
|68.1
|RA1
|-
|87
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|88
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA GeForce GTX 1660||6||1408||9.4||61.0||70.4||JFP
|-
|89
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|90
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce GTX 1650 SUPER||4||1280||10.3||64.6||74.9||TS1
|-
|91
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|92
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|93
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|NVIDIA RTX A2000 Laptop GPU
|4
|2560
|11.4
|72.6
|84.0
|SK1
|-
|94
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|95
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|96
|Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz (64 CPUs), ~2.3GHz||NVIDIA Tesla M60||16||4096||12||82.7||94.7||SM2
|-
|97
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|98
|Intel(R) Core(TM) i3-8100 CPU @ 3.60GHz ||NVIDIA Tesla K40c||12||2880||12.3||83.1||95.3||NWE
|-
|99
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA Quadro T2000||4||1024||13.3||85.2||95.5||SSM
|-
|100
|Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz (Laptop) ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|101
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs), ~3.8GHz
|NVIDIA Quadro T1000
|4
|896
|13.6
|91.3
|104.9
|CB4
|-
|102
|13th Gen Intel(R) Core(TM) i7-13800H (20 CPUs), ~2.5GHz
|NVIDIA RTX A500 Laptop GPU
|4
|2048
|14.8
|93.9
|108.7
|YB1
|-
|103
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|104
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|105
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|106
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|107
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|108
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|109
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|110
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|111
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|112
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|113
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz||NVIDIA T500||4||986||29.3||197.5||226.8||TJS
|-
|114
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|115
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|116
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||NVIDIA A10-4Q||24||8192||44.5||212.6||257.1||RBR
|-
|117
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|118
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|119
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|120
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|}

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | #
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|1

|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|73.3
|568.7
|642.0
|JW3
|-
|2
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||NVIDIA GeForce RTX 4090||24||16384||103.3||811.2||914.5||JB1
|-
|3
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz||NVIDIA GeForce RTX 4090||24||16384||105.9||813.7||919.6||TA1
|-
|4
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz||NVIDIA GeForce RTX 4090||24||16384||103.7||824.3||928.0||JW1
|-
|5
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||108.2||821.5||929.7||PSM
|-
|6
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||106.8||875.5||982.3||CR4
|-
|7
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz ||NVIDIA RTX 6000 Ada Generation||48||18176||119.6||877.8||997.4||JG3
|-
|8
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|134.0
|1049.9
|1183.9
|TM2
|-
|9
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||139.5||1115.1||1254.6||JM3
|-
|10
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|11
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||155.5||1176.2||1331.7||JMM
|-
|12
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|13
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|14
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||159.7||1216.2||1375.9||GP1
|-
|15
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||160.9||1240||1400.9||DD2
|-
|16
|Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz ||NVIDIA GeForce RTX 3090||24||10496||172.9||1249.8||1422.7||SIP
|-
|17
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|172.5
|1343.4
|15.15.9
|PW1
|-
|18
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|19
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||190.2||1493.2||1683.4||HP1
|-
|20
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|21
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|22
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|23
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|24
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||215.9||1678.7||1894.6||PA2
|-
|25
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||255.4||1691.7||1947.1||HP2
|-
|26
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|242.3
|1856.5
|2098.8
|YW1
|-
|27
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|28
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|29
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|30
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|31
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|32
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|33
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|34
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|35
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|36
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|37
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|38
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|39
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking | Back to TUFLOW Benchmarking]]
}}

2xxx TUFLOW Messages

2026-02-18T21:48:00Z

Abrar.Alttahir:

The 2xxx TUFLOW messages refer to issues that occur neither in the 2D model. Click on number to follow link to TUFLOW message description, grey numbers are reserved and do not yet have messages assigned, and red numbers have been allocated to new messages.

See also:
* [[0xxx TUFLOW Messages]]
* [[1xxx TUFLOW Messages]]
* [[3xxx TUFLOW Messages]]
* [[4xxx TUFLOW Messages]]
* [[5xxx TUFLOW Messages]]
* [[6xxx TUFLOW Messages]]

{| Border ="1" cellpadding="8" style="text-align:center"
|2000 || [[TUFLOW Message 2001|2001 ]] || [[TUFLOW Message 2002|2002 ]] || [[TUFLOW Message 2003|2003 ]] || [[TUFLOW Message 2004|2004 ]] || [[TUFLOW Message 2005|2005 ]] || [[TUFLOW Message 2006|2006 ]] || [[TUFLOW Message 2007|2007 ]] || [[TUFLOW Message 2008|2008 ]] || [[TUFLOW Message 2009|2009 ]] || [[TUFLOW Message 2010|2010 ]] || [[TUFLOW Message 2011|2011 ]] || [[TUFLOW Message 2012|2012 ]] || [[TUFLOW Message 2013|2013 ]] || [[TUFLOW Message 2014|2014 ]] || [[TUFLOW Message 2015|2015 ]] || [[TUFLOW Message 2016|2016 ]] || [[TUFLOW Message 2017|2017 ]] || [[TUFLOW Message 2018|2018 ]] || [[TUFLOW Message 2019|2019 ]]
|-
|[[TUFLOW Message 2020|2020 ]] || [[TUFLOW Message 2021|2021 ]] || [[TUFLOW Message 2022|2022 ]] || [[TUFLOW Message 2023|2023 ]] || [[TUFLOW Message 2024|2024 ]] || [[TUFLOW Message 2025|2025 ]] || [[TUFLOW Message 2026|2026 ]] || [[TUFLOW Message 2027|2027 ]] || [[TUFLOW Message 2028|2028 ]] || [[TUFLOW Message 2029|2029 ]] || [[TUFLOW Message 2030|2030 ]] || [[TUFLOW Message 2031|2031 ]] || [[TUFLOW Message 2032|2032 ]] || [[TUFLOW Message 2033|2033 ]] || [[TUFLOW Message 2034|2034 ]] || [[TUFLOW Message 2035|2035 ]] || [[TUFLOW Message 2036|2036 ]] || [[TUFLOW Message 2037|2037 ]] || [[TUFLOW Message 2038|2038 ]] || [[TUFLOW Message 2039|2039 ]]
|-
|[[TUFLOW Message 2040|2040 ]] || [[TUFLOW Message 2041|2041 ]] || [[TUFLOW Message 2042|2042 ]] || [[TUFLOW Message 2043|2043 ]] || [[TUFLOW Message 2044|2044 ]] || [[TUFLOW Message 2045|2045 ]] || [[TUFLOW Message 2046|2046 ]] || [[TUFLOW Message 2047|2047 ]] || [[TUFLOW Message 2048|2048 ]] || [[TUFLOW Message 2049|2049 ]] || [[TUFLOW Message 2050|2050 ]] || [[TUFLOW Message 2051|2051 ]] || [[TUFLOW Message 2052|2052 ]] || [[TUFLOW Message 2053|2053 ]] || [[TUFLOW Message 2054|2054 ]] || [[TUFLOW Message 2055|2055 ]] || [[TUFLOW Message 2056|2056 ]] || [[TUFLOW Message 2057|2057 ]] || [[TUFLOW Message 2058|2058 ]] || [[TUFLOW Message 2059|2059 ]]
|-
|[[TUFLOW Message 2060|2060 ]] || [[TUFLOW Message 2061|2061 ]] || [[TUFLOW Message 2062|2062 ]] || [[TUFLOW Message 2063|2063 ]] || [[TUFLOW Message 2064|2064 ]] || [[TUFLOW Message 2065|2065 ]] || [[TUFLOW Message 2066|2066 ]] || [[TUFLOW Message 2067|2067 ]] || [[TUFLOW Message 2068|2068 ]] || [[TUFLOW Message 2069|2069 ]] || [[TUFLOW Message 2070|2070 ]] || [[TUFLOW Message 2071|2071 ]] || [[TUFLOW Message 2072|2072 ]] || [[TUFLOW Message 2073|2073 ]] || [[TUFLOW Message 2074|2074 ]] || [[TUFLOW Message 2075|2075 ]] || [[TUFLOW Message 2076|2076 ]] || [[TUFLOW Message 2077|2077 ]] || [[TUFLOW Message 2078|2078 ]] || [[TUFLOW Message 2079|2079 ]]
|-
|[[TUFLOW Message 2080|2080 ]] || [[TUFLOW Message 2081|2081 ]] || [[TUFLOW Message 2082|2082 ]] || [[TUFLOW Message 2083|2083 ]] || [[TUFLOW Message 2084|2084 ]] || [[TUFLOW Message 2085|2085 ]] || [[TUFLOW Message 2086|2086 ]] || [[TUFLOW Message 2087|2087 ]] || [[TUFLOW Message 2088|2088 ]] || [[TUFLOW Message 2089|2089 ]] || [[TUFLOW Message 2090|2090 ]] || [[TUFLOW Message 2091|2091 ]] || [[TUFLOW Message 2092|2092 ]] || [[TUFLOW Message 2093|2093 ]] || [[TUFLOW Message 2094|2094 ]] || [[TUFLOW Message 2095|2095 ]] || [[TUFLOW Message 2096|2096 ]] || [[TUFLOW Message 2097|2097 ]] || [[TUFLOW Message 2098|2098 ]] || [[TUFLOW Message 2099|2099 ]]
|-
|[[TUFLOW Message 2100|2100 ]] || [[TUFLOW Message 2101|2101 ]] || [[TUFLOW Message 2102|2102 ]] || [[TUFLOW Message 2103|2103 ]] || [[TUFLOW Message 2104|2104 ]] || [[TUFLOW Message 2105|2105 ]] || [[TUFLOW Message 2106|2106 ]] || [[TUFLOW Message 2107|2107 ]] || [[TUFLOW Message 2108|2108 ]] || [[TUFLOW Message 2109|2109 ]] || [[TUFLOW Message 2110|2110 ]] || [[TUFLOW Message 2111|2111 ]] || [[TUFLOW Message 2112|2112 ]] || [[TUFLOW Message 2113|2113 ]] || [[TUFLOW Message 2114|2114 ]] || [[TUFLOW Message 2115|2115 ]] || [[TUFLOW Message 2116|2116 ]] || [[TUFLOW Message 2117|2117 ]] || [[TUFLOW Message 2118|2118 ]] || [[TUFLOW Message 2119|2119 ]]
|-
|[[TUFLOW Message 2120|2120 ]] || [[TUFLOW Message 2121|2121 ]] || [[TUFLOW Message 2122|2122 ]] || [[TUFLOW Message 2123|2123 ]] || [[TUFLOW Message 2124|2124 ]] || [[TUFLOW Message 2125|2125 ]] || [[TUFLOW Message 2126|2126 ]] || [[TUFLOW Message 2127|2127 ]] || [[TUFLOW Message 2128|2128 ]] || [[TUFLOW Message 2129|2129 ]] || [[TUFLOW Message 2130|2130 ]] || [[TUFLOW Message 2131|2131 ]] || [[TUFLOW Message 2132|2132 ]] || [[TUFLOW Message 2133|2133 ]] || [[TUFLOW Message 2134|2134 ]] || [[TUFLOW Message 2135|2135 ]] || [[TUFLOW Message 2136|2136 ]] || [[TUFLOW Message 2137|2137 ]] || [[TUFLOW Message 2138|2138 ]] || [[TUFLOW Message 2139|2139 ]]
|-
|[[TUFLOW Message 2140|2140 ]] || [[TUFLOW Message 2141|2141 ]] || [[TUFLOW Message 2142|2142 ]] || [[TUFLOW Message 2143|2143 ]] || [[TUFLOW Message 2144|2144 ]] || [[TUFLOW Message 2145|2145 ]] || [[TUFLOW Message 2146|2146 ]] || [[TUFLOW Message 2147|2147 ]] || [[TUFLOW Message 2148|2148 ]] || [[TUFLOW Message 2149|2149 ]] || [[TUFLOW Message 2150|2150 ]] || [[TUFLOW Message 2151|2151 ]] || [[TUFLOW Message 2152|2152 ]] || [[TUFLOW Message 2153|2153 ]] || [[TUFLOW Message 2154|2154 ]] || [[TUFLOW Message 2155|2155 ]] || [[TUFLOW Message 2156|2156 ]] || [[TUFLOW Message 2157|2157 ]] || [[TUFLOW Message 2158|2158 ]] || [[TUFLOW Message 2159|2159 ]]
|-
|[[TUFLOW Message 2160|2160 ]] || [[TUFLOW Message 2161|2161 ]] || [[TUFLOW Message 2162|2162 ]] || [[TUFLOW Message 2163|2163 ]] || [[TUFLOW Message 2164|2164 ]] || [[TUFLOW Message 2165|2165 ]] || [[TUFLOW Message 2166|2166 ]] || [[TUFLOW Message 2167|2167 ]] || [[TUFLOW Message 2168|2168 ]] || [[TUFLOW Message 2169|2169 ]] || [[TUFLOW Message 2170|2170 ]] || [[TUFLOW Message 2171|2171 ]] || [[TUFLOW Message 2172|2172 ]] || [[TUFLOW Message 2173|2173 ]] || [[TUFLOW Message 2174|2174 ]] || [[TUFLOW Message 2175|2175 ]] || [[TUFLOW Message 2176|2176 ]] || [[TUFLOW Message 2177|2177 ]] || [[TUFLOW Message 2178|2178 ]] || [[TUFLOW Message 2179|2179 ]]
|-
|[[TUFLOW Message 2180|2180 ]] || [[TUFLOW Message 2181|2181 ]] || [[TUFLOW Message 2182|2182 ]] || [[TUFLOW Message 2183|2183 ]] || [[TUFLOW Message 2184|2184 ]] || [[TUFLOW Message 2185|2185 ]] || [[TUFLOW Message 2186|2186 ]] || [[TUFLOW Message 2187|2187 ]] || [[TUFLOW Message 2188|2188 ]] || [[TUFLOW Message 2189|2189 ]] || [[TUFLOW Message 2190|2190 ]] || [[TUFLOW Message 2191|2191 ]] || [[TUFLOW Message 2192|2192 ]] || [[TUFLOW Message 2193|2193 ]] || [[TUFLOW Message 2194|2194 ]] || [[TUFLOW Message 2195|2195 ]] || [[TUFLOW Message 2196|2196 ]] || [[TUFLOW Message 2197|2197 ]] || [[TUFLOW Message 2198|2198 ]] || [[TUFLOW Message 2199|2199 ]]
|-
|[[TUFLOW Message 2200|2200 ]] || [[TUFLOW Message 2201|2201 ]] || [[TUFLOW Message 2202|2202 ]] || [[TUFLOW Message 2203|2203 ]] || [[TUFLOW_Message_2204| 2204]] || [[TUFLOW Message 2205|2205 ]] || [[TUFLOW Message 2206|2206 ]] || 2207 || [[TUFLOW Message 2208|2208 ]] || 2209 || [[TUFLOW Message 2210|2210 ]] || [[TUFLOW Message 2211|2211 ]] || [[TUFLOW Message 2212|2212 ]] || [[TUFLOW Message 2213|2213 ]] || [[TUFLOW Message 2214|2214 ]] || [[TUFLOW Message 2215|2215 ]] || [[TUFLOW Message 2216|2216 ]] || [[TUFLOW Message 2217|2217 ]] || [[TUFLOW Message 2218|2218 ]] || [[TUFLOW Message 2219|2219 ]]
|-
|[[TUFLOW Message 2220|2220 ]] || [[TUFLOW Message 2221|2221 ]] || [[TUFLOW Message 2222|2222 ]] || [[TUFLOW Message 2223|2223 ]] || [[TUFLOW Message 2224|2224 ]] || [[TUFLOW Message 2225|2225 ]] || [[TUFLOW Message 2226|2226 ]] || [[TUFLOW Message 2227|2227 ]] || [[TUFLOW Message 2228|2228 ]] || [[TUFLOW Message 2229|2229 ]] || [[TUFLOW Message 2230|2230 ]] || [[TUFLOW Message 2231|2231 ]] || [[TUFLOW Message 2232|2232 ]] || [[TUFLOW Message 2233|2233 ]] || [[TUFLOW Message 2234|2234 ]] || [[TUFLOW Message 2235|2235 ]] || [[TUFLOW Message 2236|2236 ]] || [[TUFLOW Message 2237|2237 ]] || [[TUFLOW Message 2238|2238 ]] || [[TUFLOW Message 2239|2239 ]]
|-
|[[TUFLOW Message 2240|2240 ]] || [[TUFLOW Message 2241|2241 ]] || [[TUFLOW Message 2242|2242 ]] || 2243 || [[TUFLOW Message 2244|2244 ]] || 2245 || [[TUFLOW Message 2246|2246 ]] || [[TUFLOW Message 2247|2247 ]] || [[TUFLOW Message 2248|2248 ]] || 2249 || 2250 || 2251 || 2252 || 2253 || 2254 || 2255 || 2256 || 2257 || 2258 || 2259
|-
|2300 || 2301 || [[TUFLOW Message 2302|2302 ]] || 2303 || [[TUFLOW Message 2304|2304 ]] || [[TUFLOW Message 2305|2305 ]] || [[TUFLOW Message 2306|2306 ]] || 2307 || [[TUFLOW Message 2308|2308 ]] || [[TUFLOW Message 2309|2309 ]] || [[TUFLOW Message 2310|2310 ]] || [[TUFLOW Message 2311|2311 ]] || 2312 || [[TUFLOW Message 2313|2313 ]] || [[TUFLOW Message 2314|2314 ]] || [[TUFLOW Message 2315|2315 ]]|| [[TUFLOW Message 2316|2316 ]] || [[TUFLOW Message 2317|2317 ]] || 2318 || [[TUFLOW Message 2319|2319 ]]
|-
|[[TUFLOW Message 2320|2320 ]] || [[TUFLOW Message 2321|2321 ]] || 2322 || [[TUFLOW Message 2323|2323 ]] || 2324 || 2325 || 2326 || 2327 || 2328 || [[TUFLOW Message 2329|2329 ]] || [[TUFLOW Message 2330|2330 ]] || [[TUFLOW Message 2331|2331 ]] || 2332 || [[TUFLOW Message 2333|2333 ]] || [[TUFLOW Message 2334|2334 ]] || [[TUFLOW Message 2335|2335 ]] || [[TUFLOW Message 2336|2336 ]] || [[TUFLOW Message 2337|2337]] || [[TUFLOW Message 2338|2338]] || 2339
|-
|[[TUFLOW Message 2340|2340 ]] || 2341 || 2342 || [[TUFLOW Message 2343|2343 ]] || 2344 || 2345 || 2346 || [[TUFLOW Message 2347|2347 ]] || [[TUFLOW Message 2348|2348 ]] || [[TUFLOW Message 2349|2349 ]] || [[TUFLOW Message 2350|2350 ]] || [[TUFLOW Message 2351|2351 ]] || [[TUFLOW Message 2352|2352 ]] || [[TUFLOW Message 2353|2353 ]] || [[TUFLOW Message 2354|2354 ]] || [[TUFLOW Message 2355|2355 ]] || [[TUFLOW Message 2356|2356 ]] || [[TUFLOW Message 2357|2357 ]] || [[TUFLOW Message 2358|2358 ]] || [[TUFLOW Message 2359|2359 ]]
|-
|[[TUFLOW Message 2360|2360 ]] || [[TUFLOW Message 2361|2361 ]] || [[TUFLOW Message 2362|2362 ]] || 2363 || 2364 || [[TUFLOW Message 2365|2365 ]] || 2366 || [[TUFLOW Message 2367|2367 ]] || [[TUFLOW Message 2368|2368 ]] || 2369 || [[TUFLOW Message 2370|2370 ]] || [[TUFLOW Message 2371|2371 ]] || [[TUFLOW Message 2372|2372 ]] || [[TUFLOW Message 2373|2373 ]] || [[TUFLOW Message 2374|2374 ]] || [[TUFLOW Message 2375|2375 ]] || [[TUFLOW Message 2376|2376 ]] || [[TUFLOW Message 2377|2377 ]] || 2378 || 2379
|-
|[[TUFLOW Message 2380|2380 ]] || [[TUFLOW Message 2381|2381 ]] || [[TUFLOW Message 2382|2382 ]] || [[TUFLOW Message 2383|2383 ]] || 2384 || [[TUFLOW Message 2385|2385 ]] || [[TUFLOW Message 2386|2386 ]] || 2387 || 2388 || [[TUFLOW Message 2389|2389 ]] || [[TUFLOW Message 2390|2390 ]] || [[TUFLOW Message 2391|2391 ]] || [[TUFLOW Message 2392|2392 ]] || 2393 || 2394 || 2395 || 2396 || 2397 || 2398 || 2399
|-
|[[TUFLOW Message 2400|2400 ]] || [[TUFLOW Message 2401|2401 ]] || [[TUFLOW Message 2402|2402 ]] || [[TUFLOW Message 2403|2403 ]] || [[TUFLOW Message 2404|2404 ]] || [[TUFLOW Message 2405|2405 ]] || [[TUFLOW Message 2406|2406 ]] || [[TUFLOW Message 2407|2407 ]] || [[TUFLOW Message 2408|2408 ]] || [[TUFLOW Message 2409|2409 ]] || 2410 || [[TUFLOW Message 2411|2411 ]] || [[TUFLOW Message 2412|2412 ]] || [[TUFLOW Message 2413|2413 ]] || [[TUFLOW Message 2414|2414 ]] || [[TUFLOW Message 2415|2415 ]] || [[TUFLOW Message 2416|2416 ]] || [[TUFLOW Message 2417|2417 ]] || [[TUFLOW Message 2418|2418 ]] || [[TUFLOW Message 2419|2419 ]]
|-
|[[TUFLOW Message 2420|2420 ]] || [[TUFLOW Message 2421|2421 ]] || [[TUFLOW Message 2422|2422 ]] || [[TUFLOW Message 2423|2423 ]] || [[TUFLOW Message 2424|2424 ]] || [[TUFLOW Message 2425|2425 ]] || [[TUFLOW Message 2426|2426 ]] || [[TUFLOW Message 2427|2427 ]] || [[TUFLOW Message 2428|2428 ]] || [[TUFLOW Message 2429|2429 ]] || [[TUFLOW Message 2430|2430 ]] || [[TUFLOW Message 2431|2431 ]] || [[TUFLOW Message 2432|2432 ]] || 2433 || 2434 || [[TUFLOW Message 2435|2435 ]] || [[TUFLOW Message 2436|2436 ]] || 2437 || [[TUFLOW Message 2438|2438 ]] || 2439
|-
|[[TUFLOW Message 2440|2440 ]] || [[TUFLOW Message 2441|2441 ]] || [[TUFLOW Message 2442|2442 ]] || [[TUFLOW Message 2443|2443 ]] || [[TUFLOW Message 2444|2444 ]] || [[TUFLOW Message 2445|2445 ]] || 2446 || [[TUFLOW Message 2447|2447 ]] || 2448 || 2449 || [[TUFLOW Message 2450|2450 ]] || [[TUFLOW Message 2451|2451 ]] || 2452 || 2453 || 2454 || 2455 || 2456 || 2457 || 2458 || 2459
|-
|[[TUFLOW Message 2460|2460 ]] || [[TUFLOW Message 2461|2461 ]] || [[TUFLOW Message 2462|2462 ]] || [[TUFLOW Message 2463|2463 ]] || [[TUFLOW Message 2464|2464 ]] || [[TUFLOW Message 2465|2465 ]] || [[TUFLOW Message 2466|2466 ]] || [[TUFLOW Message 2467|2467 ]] || [[TUFLOW Message 2468|2468 ]] || [[TUFLOW Message 2469|2469 ]] || [[TUFLOW Message 2470|2470 ]] || [[TUFLOW Message 2471|2471 ]] || [[TUFLOW Message 2472|2472 ]] || [[TUFLOW Message 2473|2473 ]] || [[TUFLOW Message 2474|2474 ]] || 2475 || [[TUFLOW Message 2476|2476 ]] || [[TUFLOW Message 2477|2477 ]] || [[TUFLOW Message 2478|2478 ]] || [[TUFLOW Message 2479|2479 ]]
|-
|[[TUFLOW Message 2480|2480 ]] || [[TUFLOW Message 2481|2481 ]] || [[TUFLOW Message 2482|2482 ]] || [[TUFLOW Message 2483|2483 ]] || [[TUFLOW Message 2484|2484 ]] || [[TUFLOW Message 2485|2485 ]] || [[TUFLOW Message 2486|2486 ]] || [[TUFLOW Message 2487|2487 ]] || 2488 || [[TUFLOW Message 2489|2489 ]] || [[TUFLOW Message 2490|2490 ]] || [[TUFLOW Message 2491|2491 ]] || [[TUFLOW Message 2492|2492 ]] || [[TUFLOW Message 2493|2493 ]] || [[TUFLOW Message 2494|2494 ]] || [[TUFLOW Message 2495|2495 ]] || [[TUFLOW Message 2496|2496 ]] || [[TUFLOW Message 2497|2497 ]] || [[TUFLOW Message 2498|2498 ]] || [[TUFLOW Message 2499|2499 ]]
|-
|[[TUFLOW Message 2500|2500 ]] || 2501 || 2502 || 2503 || 2504 || 2505 || 2506 || [[TUFLOW Message 2507|2507 ]] || [[TUFLOW Message 2508|2508 ]] || [[TUFLOW Message 2509|2509 ]] || 2510 || 2511 || 2512 || 2513 || [[TUFLOW Message 2514|2514 ]] || [[TUFLOW Message 2515|2515 ]] || [[TUFLOW Message 2516|2516 ]] || [[TUFLOW Message 2517|2517 ]] || 2518 || [[TUFLOW Message 2519|2519 ]]
|-
|2520 || 2521 || [[TUFLOW Message 2522|2522 ]] || 2523 || 2524 || 2525 || 2526 || 2527 || 2528 || 2529 || 2530 || 2531 || 2532 || 2533 || 2534 || 2535 || 2536 || 2537 || 2538 || 2539
|-
|[[TUFLOW Message 2540|2540 ]] || [[TUFLOW Message 2541|2541 ]] || 2542 || 2543 || 2544 || 2545 || 2546 || 2547 || 2548 || 2549 || [[TUFLOW Message 2550|2550 ]] || [[TUFLOW Message 2551|2551 ]] || 2552 || 2553 || 2554 || 2555 || 2556 || 2557 || 2558 || 2559
|-
|[[TUFLOW Message 2560|2560 ]] || 2561 || [[TUFLOW Message 2562|2562 ]] || 2563 || [[TUFLOW Message 2564|2564 ]] || [[TUFLOW Message 2565|2565 ]] || [[TUFLOW Message 2566|2566 ]] || [[TUFLOW Message 2567|2567 ]] || [[TUFLOW Message 2568|2568 ]] || [[TUFLOW Message 2569|2569 ]] || [[TUFLOW Message 2570|2570 ]] || [[TUFLOW Message 2571|2571 ]] || [[TUFLOW Message 2572|2572 ]] || [[TUFLOW Message 2573|2573 ]] || [[TUFLOW Message 2574|2574 ]] || [[TUFLOW Message 2575|2575 ]] || [[TUFLOW Message 2576|2576 ]] || [[TUFLOW Message 2577|2577 ]] || [[TUFLOW Message 2578|2578 ]] || [[TUFLOW Message 2579|2579 ]]
|-
|[[TUFLOW Message 2580|2580 ]] || [[TUFLOW Message 2581|2581 ]] || [[TUFLOW Message 2582|2582 ]] || [[TUFLOW Message 2583|2583 ]] || [[TUFLOW Message 2584|2584 ]] || [[TUFLOW Message 2585|2585 ]] ||[[TUFLOW Message 2586|2586 ]] || [[TUFLOW Message 2587|2587 ]] || [[TUFLOW Message 2588|2588 ]] || [[TUFLOW Message 2589|2589 ]] || [[TUFLOW Message 2590|2590 ]] || 2591 ||[[TUFLOW Message 2592|2592 ]] || 2593 || [[TUFLOW Message 2594|2594 ]] || 2595 || 2596 || 2597 ||[[TUFLOW_Message_2598|2598]] || [[TUFLOW_Message_2599|2599]]
|-
|[[TUFLOW Message 2600|2600 ]] || [[TUFLOW Message 2601|2601 ]] || [[TUFLOW Message 2602|2602 ]] || [[TUFLOW Message 2603|2603 ]] || [[TUFLOW Message 2604|2604 ]] || [[TUFLOW Message 2605|2605 ]] || [[TUFLOW Message 2606|2606 ]] || [[TUFLOW Message 2607|2607 ]] || [[TUFLOW Message 2608|2608 ]] || [[TUFLOW Message 2609|2609 ]] || [[TUFLOW Message 2610|2610 ]] || [[TUFLOW Message 2611|2611 ]] || [[TUFLOW Message 2612|2612 ]] || [[TUFLOW Message 2613|2613 ]] || [[TUFLOW Message 2614|2614 ]] || [[TUFLOW Message 2615|2615 ]] || [[TUFLOW Message 2616|2616 ]] || [[TUFLOW Message 2617|2617 ]] || [[TUFLOW Message 2618|2618 ]] || [[TUFLOW Message 2619|2619 ]]
|-
|[[TUFLOW Message 2620|2620 ]] || 2621 || 2622 || 2623 || 2624 || 2625 || 2626 || 2627 || 2628 || 2629 || 2630 || [[TUFLOW Message 2631|2631 ]] || 2632 || 2633 || 2634 || 2635 || 2636 || 2637 || 2638 || 2639
|-
|2640 || [[TUFLOW Message 2641|2641 ]] || [[TUFLOW Message 2642|2642 ]] || 2643 || 2644 || 2645 || 2646 || 2647 || 2648 || [[TUFLOW Message 2649|2649 ]] || 2650 || 2651 || 2652 || 2653 || 2654 || 2655 || 2656 || 2657 || 2658 || 2659
|-
|[[TUFLOW Message 2660|2660 ]] || [[TUFLOW Message 2661|2661 ]] || [[TUFLOW Message 2662|2662 ]] || 2663 || 2664 || 2665 || 2666 || 2667 || 2668 || 2669 || [[TUFLOW Message 2670|2670 ]] || [[TUFLOW Message 2671|2671 ]] || 2672 || 2673 || 2674 || 2675 || 2676 || 2677 || 2678 || 2679
|-
| [[TUFLOW Message 2720|2720 ]] || 2721 || [[TUFLOW Message 2722|2722 ]] || 2723 || 2724 || 2725 || 2726 || 2727 || 2728 || 2729 || 2730 || 2731 || 2732 || 2733 || 2734 || [[TUFLOW Message 2735|2735 ]] || 2736 || 2737 || 2738 || 2739
|-
|2760 || 2761 || 2762 || [[TUFLOW Message 2763|2763 ]] || 2764 || 2765 || 2766 || 2767 || 2768 || 2769 || 2770 || 2771 || 2772 || 2773 || 2774 || 2775 || 2776 || 2777 || 2778 || 2779
|-
|2780 || 2781 || [[TUFLOW Message 2782|2782 ]] || 2783 || [[TUFLOW Message 2784|2784 ]] || [[TUFLOW Message 2785|2785 ]] || [[TUFLOW Message 2786|2786 ]] || [[TUFLOW Message 2787|2787 ]] || 2788 || 2789 || 2790 || 2791 || 2792 || 2793 || 2794 || 2795 || 2796 || 2797 || 2798 || 2799
|-
|[[TUFLOW Message 2800|2800 ]] || [[TUFLOW Message 2801|2801 ]] || [[TUFLOW Message 2802|2802 ]] || [[TUFLOW Message 2803|2803 ]] || [[TUFLOW Message 2804|2804 ]] || [[TUFLOW Message 2805|2805 ]] || [[TUFLOW Message 2806|2806 ]] || [[TUFLOW Message 2807|2807 ]] || [[TUFLOW Message 2808|2808 ]] || [[TUFLOW Message 2809|2809 ]] || [[TUFLOW Message 2810|2810 ]] || [[TUFLOW Message 2811|2811 ]] || [[TUFLOW Message 2812|2812 ]] || [[TUFLOW Message 2813|2813 ]] || [[TUFLOW Message 2814|2814 ]] || [[TUFLOW Message 2815|2815 ]] || [[TUFLOW Message 2816|2816 ]] || [[TUFLOW Message 2817|2817 ]] || [[TUFLOW Message 2818|2818 ]] || [[TUFLOW Message 2819|2819 ]]
|-
|[[TUFLOW Message 2820|2820 ]] || [[TUFLOW Message 2821|2821 ]] || [[TUFLOW Message 2822|2822 ]] || [[TUFLOW Message 2823|2823 ]] || [[TUFLOW Message 2824|2824 ]] || [[TUFLOW Message 2825|2825 ]] || [[TUFLOW Message 2826|2826 ]] || [[TUFLOW Message 2827|2827 ]] || [[TUFLOW Message 2828|2828 ]] || [[TUFLOW Message 2829|2829 ]] || [[TUFLOW Message 2830|2830 ]] || [[TUFLOW Message 2831|2831 ]] || [[TUFLOW Message 2832|2832 ]] || [[TUFLOW Message 2833|2833 ]] || [[TUFLOW Message 2834|2834 ]] || [[TUFLOW Message 2835|2835 ]] || [[TUFLOW Message 2836|2836 ]] || [[TUFLOW Message 2837|2837 ]] || [[TUFLOW Message 2838|2838 ]] || [[TUFLOW Message 2839|2839 ]]
|-
|[[TUFLOW Message 2840|2840 ]] || [[TUFLOW Message 2841|2841 ]] || [[TUFLOW Message 2842|2842 ]] || [[TUFLOW Message 2843|2843 ]] || [[TUFLOW Message 2844|2844 ]] || [[TUFLOW Message 2845|2845 ]] || [[TUFLOW Message 2846|2846 ]] || [[TUFLOW Message 2847|2847 ]] || [[TUFLOW Message 2848|2848 ]] || [[TUFLOW Message 2849|2849 ]] || [[TUFLOW Message 2850|2850 ]] || [[TUFLOW Message 2851|2851 ]] || [[TUFLOW Message 2852|2852 ]] || [[TUFLOW Message 2853|2853 ]] || [[TUFLOW Message 2854|2854 ]] || [[TUFLOW Message 2855|2855 ]] || [[TUFLOW Message 2856|2856 ]] || [[TUFLOW Message 2857|2857 ]] || [[TUFLOW Message 2858|2858 ]] || [[TUFLOW Message 2859|2859 ]]
|-
| 2900
|| 2901
|| [[TUFLOW Message 2902|2902]]
|| 2903
|| [[TUFLOW Message 2904|2904]]
|| [[TUFLOW Message 2905|2905]]
|| 2906
|| 2907
|| 2908
|| 2909
|| [[TUFLOW Message 2910|2910]]
|| [[TUFLOW Message 2911|2911]]
|| [[TUFLOW Message 2912|2912]]
|| [[TUFLOW Message 2913|2913]]
|| 2914
|| 2915
|| 2916
|| 2917
|| 2918
|| 2919
|-
|[[TUFLOW Message 2920|2920 ]] || [[TUFLOW Message 2921|2921 ]] || [[TUFLOW Message 2922|2922 ]] || [[TUFLOW Message 2923|2923 ]] || [[TUFLOW Message 2924|2924 ]] || [[TUFLOW Message 2925|2925 ]]|| [[TUFLOW Message 2926|2926 ]] || [[TUFLOW Message 2927|2927 ]] || [[TUFLOW Message 2928|2928 ]] || [[TUFLOW Message 2929|2929 ]] || [[TUFLOW Message 2930|2930 ]] || [[TUFLOW Message 2931|2931 ]] || [[TUFLOW Message 2932|2932 ]] || [[TUFLOW Message 2933|2933 ]] || [[TUFLOW Message 2934|2934 ]] || [[TUFLOW Message 2935|2935 ]] || [[TUFLOW Message 2936|2936 ]] || [[TUFLOW Message 2937|2937 ]] || [[TUFLOW Message 2938|2938 ]] || 2939
|-
|[[TUFLOW Message 2980|2980 ]] || 2981 || 2982 || 2983 || 2984 || 2985 || 2986 || 2987 || 2988 || 2989 || 2990 || [[TUFLOW Message 2991|2991 ]] || 2992 || 2993 || 2994 || 2995 || 2996 || 2997 || [[TUFLOW Message 2998|2998 ]] || [[TUFLOW Message 2999|2999 ]]
|-
|}
 
{{Tips Navigation
|uplink=[[ Main_Page | Back to Main Page]]
}}

1xxx TUFLOW Messages

2026-02-18T21:44:35Z

Abrar.Alttahir:

The 1xxx error messages refer to errors that occur in the 1D part of the model. Links to detailed message descriptions are listed in the table below. Grey numbers do not yet have a message assigned and red numbers have been allocated to new messages.

See also:
* [[0xxx TUFLOW Messages]]
* [[2xxx TUFLOW Messages]]
* [[3xxx TUFLOW Messages]]
* [[4xxx TUFLOW Messages]]
* [[5xxx TUFLOW Messages]]
* [[6xxx TUFLOW Messages]]

{| Border ="1" cellpadding="8" style="text-align:center"
|1000 || [[TUFLOW Message 1001|1001 ]] || [[TUFLOW Message 1002|1002 ]] || [[TUFLOW Message 1003|1003 ]] || [[TUFLOW Message 1004|1004 ]] || [[TUFLOW Message 1005|1005 ]] || [[TUFLOW Message 1006|1006 ]] || [[TUFLOW Message 1007|1007 ]] || [[TUFLOW Message 1008|1008 ]] || [[TUFLOW Message 1009|1009 ]] || [[TUFLOW Message 1010|1010 ]] || [[TUFLOW Message 1011|1011 ]] || [[TUFLOW Message 1012|1012 ]] || [[TUFLOW Message 1013|1013 ]] || [[TUFLOW Message 1014|1014 ]] || [[TUFLOW Message 1015|1015 ]] || [[TUFLOW Message 1016|1016 ]] || [[TUFLOW Message 1017|1017 ]] || [[TUFLOW Message 1018|1018 ]] || [[TUFLOW Message 1019|1019 ]]
|-
|[[TUFLOW Message 1020|1020 ]] || [[TUFLOW Message 1021|1021 ]] || [[TUFLOW Message 1022|1022 ]] || [[TUFLOW Message 1023|1023 ]] || [[TUFLOW Message 1024|1024 ]] || [[TUFLOW Message 1025|1025 ]] || [[TUFLOW Message 1026|1026 ]] || [[TUFLOW Message 1027|1027 ]] || [[TUFLOW Message 1028|1028 ]] || [[TUFLOW Message 1029|1029 ]] || [[TUFLOW Message 1030|1030 ]] || [[TUFLOW Message 1031|1031 ]] || [[TUFLOW Message 1032|1032 ]] || [[TUFLOW Message 1033|1033 ]] || [[TUFLOW Message 1034|1034 ]] || [[TUFLOW Message 1035|1035 ]] || [[TUFLOW Message 1036|1036 ]] || [[TUFLOW Message 1037|1037 ]] || [[TUFLOW Message 1038|1038 ]] || [[TUFLOW Message 1039|1039 ]]
|-
|[[TUFLOW Message 1040|1040 ]] || [[TUFLOW Message 1041|1041 ]] || [[TUFLOW Message 1042|1042 ]] || [[TUFLOW Message 1043|1043 ]] || [[TUFLOW Message 1044|1044 ]] || [[TUFLOW Message 1045|1045 ]] || 1046 || 1047 || [[TUFLOW Message 1048|1048 ]] || [[TUFLOW Message 1049|1049 ]] || [[TUFLOW Message 1050|1050 ]] || [[TUFLOW Message 1051|1051 ]] || [[TUFLOW Message 1052|1052 ]] || [[TUFLOW Message 1053|1053 ]] || [[TUFLOW Message 1054|1054 ]] || [[TUFLOW Message 1055|1055 ]] || [[TUFLOW Message 1056|1056 ]] || [[TUFLOW Message 1057|1057 ]] || [[TUFLOW Message 1058|1058 ]] || [[TUFLOW Message 1059|1059 ]]
|-
|[[TUFLOW Message 1060|1060 ]] || [[TUFLOW Message 1061|1061 ]] || [[TUFLOW Message 1062|1062 ]] || [[TUFLOW Message 1063|1063 ]] || [[TUFLOW Message 1064|1064 ]] || [[TUFLOW Message 1065|1065 ]] || [[TUFLOW Message 1066|1066 ]] || [[TUFLOW Message 1067|1067 ]] || [[TUFLOW Message 1068|1068 ]] || [[TUFLOW Message 1069|1069 ]] || [[TUFLOW Message 1070|1070 ]] || [[TUFLOW Message 1071|1071 ]] || [[TUFLOW Message 1072|1072 ]] || [[TUFLOW Message 1073|1073 ]] || [[TUFLOW Message 1074|1074 ]] || [[TUFLOW Message 1075|1075 ]] || [[TUFLOW Message 1076|1076 ]] || [[TUFLOW Message 1077|1077 ]] || [[TUFLOW Message 1078|1078 ]] || [[TUFLOW Message 1079|1079 ]]
|-
|[[TUFLOW Message 1080|1080 ]] || [[TUFLOW Message 1081|1081 ]] || [[TUFLOW Message 1082|1082 ]] || [[TUFLOW Message 1083|1083 ]] || [[TUFLOW Message 1084|1084 ]] || [[TUFLOW Message 1085|1085 ]] || 1086 || 1087 || [[TUFLOW Message 1088|1088 ]] || [[TUFLOW Message 1089|1089 ]] || [[TUFLOW Message 1090|1090 ]] || [[TUFLOW Message 1091|1091 ]] || [[TUFLOW Message 1092|1092 ]] || [[TUFLOW Message 1093|1093 ]] || 1094 || [[TUFLOW Message 1095|1095 ]] || [[TUFLOW Message 1096|1096 ]] || [[TUFLOW Message 1097|1097 ]] || [[TUFLOW Message 1098|1098 ]] || [[TUFLOW Message 1099|1099 ]]
|-
| [[TUFLOW Message 1100|1100 ]] || [[TUFLOW Message 1101|1101 ]] || [[TUFLOW Message 1102|1102 ]] || [[TUFLOW Message 1103|1103 ]] || [[TUFLOW Message 1104|1104 ]] || [[TUFLOW Message 1105|1105 ]] || [[TUFLOW Message 1106|1106 ]] || [[TUFLOW Message 1107|1107 ]] || [[TUFLOW Message 1108|1108 ]] || [[TUFLOW Message 1109|1109 ]]|| [[TUFLOW Message 1110|1110 ]] || [[TUFLOW Message 1111|1111 ]] || [[TUFLOW Message 1112|1112 ]] || [[TUFLOW Message 1113|1113 ]] || [[TUFLOW Message 1114|1114 ]] || [[TUFLOW Message 1115|1115 ]] || [[TUFLOW Message 1116|1116 ]] || [[TUFLOW Message 1117|1117 ]] || [[TUFLOW Message 1118|1118 ]] || [[TUFLOW Message 1119|1119 ]]
|-
|[[TUFLOW Message 1120|1120 ]] || [[TUFLOW Message 1121|1121 ]] || [[TUFLOW Message 1122|1122 ]] || [[TUFLOW Message 1123|1123 ]] || [[TUFLOW Message 1124|1124 ]] || [[TUFLOW Message 1125|1125 ]] || [[TUFLOW Message 1126|1126 ]] || [[TUFLOW Message 1127|1127 ]] || [[TUFLOW Message 1128|1128 ]] || [[TUFLOW Message 1129|1129 ]] || 1130 || [[TUFLOW Message 1131|1131 ]] || [[TUFLOW Message 1132|1132 ]] || [[TUFLOW Message 1133|1133 ]] || [[TUFLOW Message 1134|1134 ]] || [[TUFLOW Message 1135|1135 ]] || [[TUFLOW Message 1136|1136 ]] || [[TUFLOW Message 1137|1137 ]] || [[TUFLOW Message 1138|1138 ]] || [[TUFLOW Message 1139|1139 ]]
|-
|[[TUFLOW Message 1140|1140 ]] || [[TUFLOW Message 1141|1141 ]] || [[TUFLOW Message 1142|1142 ]] || [[TUFLOW Message 1143|1143 ]] || [[TUFLOW Message 1144|1144 ]] || [[TUFLOW Message 1145|1145 ]] || [[TUFLOW Message 1146|1146 ]] || 1147 || 1148 || 1149 || 1150 || [[TUFLOW Message 1151|1151 ]] || [[TUFLOW Message 1152|1152 ]] || [[TUFLOW Message 1153|1153 ]] || [[TUFLOW Message 1154|1154 ]] || 1155 || [[TUFLOW Message 1156|1156 ]] || [[TUFLOW Message 1157|1157 ]] || [[TUFLOW Message 1158|1158 ]] || [[TUFLOW Message 1159|1159 ]]
|-
|1160 || [[TUFLOW Message 1161|1161 ]] || [[TUFLOW Message 1162|1162 ]] || [[TUFLOW Message 1163|1163 ]] || [[TUFLOW Message 1164|1164 ]] || 1165 || 1166 || 1167 || 1168 || 1169 || 1170 || [[TUFLOW Message 1171|1171 ]] || 1172 || 1173 || 1174 || 1175 || 1176 || [[TUFLOW Message 1177|1177 ]] || [[TUFLOW Message 1178|1178 ]] || [[TUFLOW Message 1179|1179 ]]
|-
|1180 || [[TUFLOW Message 1181|1181 ]] || [[TUFLOW Message 1182|1182 ]] || [[TUFLOW Message 1183|1183 ]] || 1184 || [[TUFLOW Message 1185|1185 ]] || 1186 || 1187 || [[TUFLOW Message 1188|1188 ]] || [[TUFLOW Message 1189|1189 ]] || 1190 || [[TUFLOW Message 1191|1191 ]] || [[TUFLOW Message 1192|1192 ]] || [[TUFLOW Message 1193|1193 ]] || [[TUFLOW Message 1194|1194 ]] || 1195 || 1196 || 1197 || 1198 || 1199
|-
|[[TUFLOW Message 1200|1200 ]] || [[TUFLOW Message 1201|1201 ]] || [[TUFLOW Message 1202|1202 ]] || [[TUFLOW Message 1203|1203 ]] || [[TUFLOW Message 1204|1204 ]] || [[TUFLOW Message 1205|1205 ]] || [[TUFLOW Message 1206|1206 ]] || [[TUFLOW Message 1207|1207 ]] || [[TUFLOW Message 1208|1208 ]] || [[TUFLOW Message 1209|1209 ]] || 1210 || [[TUFLOW Message 1211|1211 ]] || [[TUFLOW Message 1212|1212 ]] || [[TUFLOW Message 1213|1213 ]] || [[TUFLOW Message 1214|1214 ]] || 1215 || 1216 || 1217 || [[TUFLOW Message 1218|1218 ]] || [[TUFLOW Message 1219|1219 ]]
|-
|[[TUFLOW Message 1220|1220 ]] || [[TUFLOW Message 1221|1221 ]] || 1222 || [[TUFLOW Message 1223|1223 ]] || 1224 || 1225 || 1226 || 1227 || [[TUFLOW Message 1228|1228 ]] || [[TUFLOW Message 1229|1229 ]] || 1230 || [[TUFLOW Message 1231|1231 ]] || [[TUFLOW Message 1232|1232 ]] || [[TUFLOW Message 1233|1233 ]] || 1234 || 1235 || 1236 || 1237 || [[TUFLOW Message 1238|1238 ]] || [[TUFLOW Message 1239|1239 ]]
|-
|1240 || [[TUFLOW Message 1241|1241 ]] || [[TUFLOW Message 1242|1242 ]] || [[TUFLOW Message 1243|1243 ]] || 1244 || [[TUFLOW Message 1245|1245 ]] || 1246 || 1247 || 1248 || 1249 || 1250 || [[TUFLOW Message 1251|1251 ]] || [[TUFLOW Message 1252|1252 ]] || [[TUFLOW Message 1253|1253 ]] || 1254 || [[TUFLOW Message 1255|1255 ]] || [[TUFLOW Message 1256|1256 ]] || [[TUFLOW Message 1257|1257 ]] || [[TUFLOW Message 1258|1258 ]] || 1259
|-
|1260 || [[TUFLOW Message 1261|1261 ]] || [[TUFLOW Message 1262|1262 ]] || 1263 || [[TUFLOW Message 1264|1264 ]] || [[TUFLOW Message 1265|1265 ]] || [[TUFLOW Message 1266|1266 ]] || [[TUFLOW Message 1267|1267 ]] || [[TUFLOW Message 1268|1268 ]] || [[TUFLOW Message 1269|1269 ]] || [[TUFLOW Message 1270|1270 ]] || [[TUFLOW Message 1271|1271 ]] || [[TUFLOW Message 1272|1272 ]] || 1273 || [[TUFLOW Message 1274|1274 ]] || [[TUFLOW Message 1275|1275 ]] || [[TUFLOW Message 1276|1276 ]] || [[TUFLOW Message 1277|1277 ]] || [[TUFLOW Message 1278|1278 ]] || [[TUFLOW Message 1279|1279 ]]
|-
|[[TUFLOW Message 1280|1280 ]] || [[TUFLOW Message 1281|1281 ]] || [[TUFLOW Message 1282|1282 ]] || [[TUFLOW Message 1283|1283 ]] || [[TUFLOW Message 1284|1284 ]] || [[TUFLOW Message 1285|1285 ]] || [[TUFLOW Message 1286|1286 ]] || [[TUFLOW Message 1287|1287 ]] || [[TUFLOW Message 1288|1288 ]] || [[TUFLOW Message 1289|1289 ]] || [[TUFLOW Message 1290|1290 ]] || [[TUFLOW Message 1291|1291 ]] || [[TUFLOW Message 1292|1292 ]] || [[TUFLOW Message 1293|1293 ]] || [[TUFLOW Message 1294|1294 ]] || [[TUFLOW Message 1295|1295 ]] || [[TUFLOW Message 1296|1296 ]] || [[TUFLOW Message 1297|1297 ]] || [[TUFLOW Message 1298|1298 ]] || [[TUFLOW Message 1299|1299 ]]
|-
|1300 || [[TUFLOW Message 1301|1301 ]] || [[TUFLOW Message 1302|1302 ]] || [[TUFLOW Message 1303|1303 ]] || [[TUFLOW Message 1304|1304 ]] || [[TUFLOW Message 1305|1305 ]] || [[TUFLOW Message 1306|1306 ]] || 1307 || [[TUFLOW Message 1308|1308 ]] || [[TUFLOW Message 1309|1309 ]] || 1310 || [[TUFLOW Message 1311|1311 ]] || [[TUFLOW Message 1312|1312 ]] || [[TUFLOW Message 1313|1313 ]] || [[TUFLOW Message 1314|1314 ]] || [[TUFLOW Message 1315|1315 ]] || [[TUFLOW Message 1316|1316 ]] || [[TUFLOW Message 1317|1317 ]] || [[TUFLOW Message 1318|1318 ]] || 1319
|-
|1320 || [[TUFLOW Message 1321|1321 ]] || [[TUFLOW Message 1322|1322 ]] || [[TUFLOW Message 1323|1323 ]] || 1324 || 1325 || 1326 || 1327 || 1328 || 1329 || 1330 || [[TUFLOW Message 1331|1331 ]] || [[TUFLOW Message 1332|1332 ]] || [[TUFLOW Message 1333|1333 ]] || [[TUFLOW Message 1334|1334 ]] || [[TUFLOW Message 1335|1335 ]] || [[TUFLOW Message 1336|1336 ]] || [[TUFLOW Message 1337|1337 ]] || [[TUFLOW Message 1338|1338 ]] || [[TUFLOW Message 1339|1339 ]]
|-
|[[TUFLOW Message 1340|1340 ]] || [[TUFLOW Message 1341|1341 ]] || [[TUFLOW Message 1342|1342 ]] || [[TUFLOW Message 1343|1343 ]] || [[TUFLOW Message 1344|1344 ]] || [[TUFLOW Message 1345|1345 ]] || [[TUFLOW Message 1346|1346 ]] || [[TUFLOW Message 1347|1347 ]] || [[TUFLOW Message 1348|1348 ]] || 1349 || 1350 || [[TUFLOW Message 1351|1351 ]] || [[TUFLOW Message 1352|1352 ]] || [[TUFLOW Message 1353|1353 ]] || 1354 || [[TUFLOW Message 1355|1355 ]] || [[TUFLOW Message 1356|1356 ]] || 1357 || 1358 || 1359
|-
|1360 || [[TUFLOW Message 1361|1361 ]] || [[TUFLOW Message 1362|1362 ]] || [[TUFLOW Message 1363|1363 ]] || 1364 || 1365 || 1366 || 1367 || 1368 || 1369 || [[TUFLOW Message 1370|1370 ]] || [[TUFLOW Message 1371|1371 ]] || 1372 || 1373 || 1374 || 1375 || 1376 || 1377 || 1378 || 1379
|-
|1380 || [[TUFLOW Message 1381|1381 ]] || [[TUFLOW Message 1382|1382 ]] || 1383 || 1384 || 1385 || 1386 || 1387 || 1388 || 1389 || 1390 || [[TUFLOW Message 1391|1391 ]] || [[TUFLOW Message 1392|1392 ]] || [[TUFLOW Message 1393|1393 ]] || [[TUFLOW Message 1394|1394 ]] || 1395 || 1396 || 1397 || 1398 || 1399
|-
|1400 || [[TUFLOW Message 1401|1401 ]] || [[TUFLOW Message 1402|1402 ]] || [[TUFLOW Message 1403|1403 ]] || 1404 || [[TUFLOW Message 1405|1405 ]] || 1406 || 1407 || 1408 || 1409 || 1410 || 1411 || 1412 || 1413 || 1414 || 1415 || 1416 || 1417 || 1418 || 1419
|-
|1420 || [[TUFLOW Message 1421|1421 ]] || [[TUFLOW Message 1422|1422 ]] || [[TUFLOW Message 1423|1423 ]] || [[TUFLOW Message 1424|1424 ]] || [[TUFLOW Message 1425|1425 ]] || [[TUFLOW Message 1426|1426 ]] || [[TUFLOW Message 1427|1427 ]] || [[TUFLOW Message 1428|1428 ]] || [[TUFLOW Message 1429|1429 ]] || [[TUFLOW Message 1430|1430]] || [[TUFLOW Message 1431|1431]] || [[TUFLOW Message 1432|1432]] || [[TUFLOW Message 1433|1433]] || [[TUFLOW Message 1434|1434]] || [[TUFLOW Message 1435|1435 ]] || [[TUFLOW Message 1436|1436 ]] || [[TUFLOW Message 1437|1437 ]] || [[TUFLOW Message 1438|1438]] || [[TUFLOW Message 1439|1439 ]]
|-
|[[TUFLOW Message 1440|1440 ]] || [[TUFLOW Message 1441|1441 ]] || [[TUFLOW Message 1442|1442 ]] || [[TUFLOW Message 1443|1443 ]] || 1444 || 1445 || 1446 || 1447 || 1448 || 1449 || [[TUFLOW Message 1450|1450 ]] || [[TUFLOW Message 1451|1451 ]] || 1452 || 1453 || 1454 || [[TUFLOW Message 1455|1455 ]] || 1456 || [[TUFLOW Message 1457|1457 ]] || [[TUFLOW Message 1458|1458 ]] || [[TUFLOW Message 1459|1459 ]]
|-
| [[TUFLOW Message 1460|1460]]
|| [[TUFLOW Message 1461|1461]]
|| [[TUFLOW Message 1462|1462]]
|| [[TUFLOW Message 1463|1463]]
|| [[TUFLOW Message 1464|1464]]
|| [[TUFLOW Message 1465|1465]]
|| [[TUFLOW Message 1466|1466]]
|| [[TUFLOW Message 1467|1467]]
|| 1468
|| [[TUFLOW Message 1469|1469]]
|| 1470
|| 1471
|| 1472
|| 1473
|| 1474
|| 1475
|| 1476
|| 1477
|| 1478
|| 1479
|-
|1500 || 1501 || 1502 || 1503 || 1504 || 1505 || 1506 || 1507 || 1508 || 1509 || [[TUFLOW Message 1510|1510 ]] || 1511 || 1512 || [[TUFLOW Message 1513|1513 ]] || 1514 || 1515 || 1516 || [[TUFLOW Message 1517|1517 ]] || 1518 || 1519
|-
|1520 || [[TUFLOW Message 1521|1521 ]] || [[TUFLOW Message 1522|1522 ]] || 1523 || 1524 || [[TUFLOW Message 1525|1525 ]] || 1526 || [[TUFLOW Message 1527|1527 ]] || [[TUFLOW Message 1528|1528 ]] || 1529 || 1530 || 1531 || 1532 || 1533 || 1534 || 1535 || 1536 || 1537 || 1538 || 1539
|-
|1600 || 1601 || 1602 || 1603 || 1604 || 1605 || 1606 || 1607 || 1608 || 1609 || 1610 || 1611 || 1612 || 1613 || 1614 || 1615 || 1616 || 1617 || 1618 || [[TUFLOW Message 1619|1619 ]]
|-
|1620 || [[TUFLOW Message 1621|1621 ]] || [[TUFLOW Message 1622|1622 ]] || 1623 || 1624 || [[TUFLOW Message 1625|1625 ]] || [[TUFLOW Message 1626|1626 ]] || 1627 || [[TUFLOW Message 1628|1628 ]] || 1629 || 1630 || 1631 || 1632 || 1633 || 1634 || 1635 || 1636 || 1637 || 1638 || 1639
|-
|1700 || [[TUFLOW Message 1701|1701 ]] || 1702 || 1703 || 1704 || 1705 || 1706 || 1707 || 1708 || 1709 || 1710 || 1711 || 1712 || 1713 || 1714 || 1715 || 1716 || 1717 || 1718 || 1719
|-
|1980 || 1981 || 1982 || 1983 || 1984 || 1985 || 1986 || 1987 || 1988 || 1989 || 1990 || [[TUFLOW Message 1991|1991 ]] || 1992 || 1993 || 1994 || 1995 || [[TUFLOW Message 1996|1996 ]] || 1997 || [[TUFLOW Message 1998|1998 ]] || [[TUFLOW Message 1999|1999 ]]
|-
|}
 
{{Tips Navigation
|uplink=[[ Main_Page | Back to Main Page]]
}}

TUFLOW Output Discussion

2026-02-17T23:56:27Z

Abrar.Alttahir: /* How should below ground water surface elevation (WSE) in wet SGS cells be interpreted or presented? */

= Common Questions Answered (FAQ)=
== Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)? ==
Slight differences are expected between the result grids (ASC or FLT) and mesh results (XMDF or DAT) due to small differences in the interpolation method.
* When outputting directly to FLT or ASC using the <tt>Map Output Format</tt> command, the output grid results are derived from both the cell centres and cell corners and the final results has those values interpolated to the centres of half cells (by default).
* When outputting XMDF (or DAT) format, the mesh results are derived from cell centres for depths / levels and cell sides for vector data. The final XMDF stores data interpolated to the cell corners and doesn’t have any memory of where the corner data were derived from and what those values were.
The differences can also be amplified when the rotation of the model is not perfectly north-south aligned (i.e., a non-zero orientation). 

== Why are grids processed with TUFLOW_to_GIS different from TUFLOW grids? ==
Slight differences are expected between the result grids (ASC or FLT) produced directly from TUFLOW and post-processed from the TUFLOW_to_GIS utility from XMDF or DAT due to differences in the interpolation approach.
Check [[TUFLOW_Output_Discussion#Why_are_grid_outputs_.28ASC.2C_FLT.29_slightly_different_from_mesh_outputs_.28XMDF.2C_DAT.29.3F | Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)?]] question above on differences between outputs direct from the TUFLOW Engine. 
When ASC/FLT results are sampled from the XMDF files with the TUFLOW_to_GIS utility, it uses less data points than when the grids are written directly from TUFLOW.

The differences can also be amplified when the rotation of the model is a non-zero value.
The extent of the grids is also different. When TUFLOW starts it removes any redundant areas (if any) outside of 2d_code layer to reduce the calculation time:
* The extent of grids written directly from TUFLOW works with the unreduced area, encapsulating the entire rotated model domain in a north-south orientated rectangle.
* The extent of XMDF (or DAT) results is based on the reduced area. When the TUFLOW_to_GIS utility is used the post-processed north-south aligned grids encapsulate the reduced model extent. The origin coordinates of such grids doesn't match the origin of the model (model domain), neither the grids written out from TUFLOW directly.

In general, a better interpolation of results is achieved when the grids are written directly from TUFLOW than when post-processed with TUFLOW_to_GIS utility. Importantly, when comparing results the recommendation is to be consistent and to use the same method for the duration of a project to ensure there are no differences as a consequence of the post-processing method. 

== Why is Z0 hazard coming directly from TUFLOW different to the one calculated from maximum velocity and depth? ==
The maximum velocity and maximum depth doesn’t necessarily occur at the same time. The Z0 hazard written directly from TUFLOW has the depth x velocity calculated every timestep and at the end outputs the maximums of all the timesteps. When using the velocity and depth grids the maximum velocity is always used with maximum depth. 
Outputting the Z0 hazard grid directly from TUFLOW is more accurate, however, if this is not an option, manual creation is an acceptable workaround. The general recommendation is to choose one method and keep it consistent for the length of the project. 
When calculating the Z0 hazard from the velocity and depth, the grids have already been interpolated by TUFLOW before the raster calculation (less data is used for the calculation), whereas the TUFLOW Z0 output is interpolated from the calculated data directly. Please also refer to the above question for more information [[TUFLOW_Output_Discussion#Why_are_grid_outputs_.28ASC.2C_FLT.29_slightly_different_from_mesh_outputs_.28XMDF.2C_DAT.29.3F | Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)?]]. 

== Why does hazard output (Z1, Z2, ...) show float values and not just integers? ==
The hazard values are calculated in cell corners and are in a form of an integer. When the values are processed into a grid, which has, by default, a resolution of half the cell size, the values are interpolated to the cell centres - four cell centre values per cell. Further interpolation occurs when the model has a non-zero orientation as grid files are north-south aligned. 

== Why are results from 2d_po features, POMM.csv and plots extracted from XMDF in GIS package not matching? ==
There are numerous reasons for the outputs to not be matching:
* Plot output:
** The 2d_po results are tracked at every <tt>Time Series Output Interval</tt> and saved into the PO.csv file. If the water level peaks in between the intervals, that maximum water level won’t be recorded in the PO.csv.
** The POMM.csv tracks maximums of PO features at every computational timestep. 
* Map output:
** The temporal XMDF (or DAT) results are tracked every <tt>Map Output Interval</tt> and interpolated to the cell corners.
** The maximum XMDF map output records the value at every computational timestep, however the final maximum map output is also being interpolated to the cell corners. 
** The extraction tools in GIS packages use interpolated data for the plot creation, e.g. less data than the TUFLOW direct plot output.
* Although the maximums from POMM.csv and the maximum map output will be very close, they won't match exactly.

Further notes for using post-processed flux line:
* No information is stored in the map output about whether the flow is upstream or downstream controlled.
* The staggered computational grid means there is always some interpolation occurring when projecting levels and velocities to the flux line. This will cause significant uncertainty in areas of sudden topographic change such as at an embankment. It is also the reason that two similar lines side by side may provide different outputs.
* Highly variable/complex flows, which are usually associated with highly variable topography, will add to the uncertainty when interpolating to the flux line.

The general recommendation is to use a lot of 2d_po features with reasonably small <tt>Time Series Output Interval</tt> to achieve better accuracy, they won't slow the simulation down. In case of flux lines, the 2d_po lines are using the actual computed fluxes across each cell face and will be an exact representation of the flow being calculated by the 2D solver. Post-processed fluxes should only be located in areas of little topographic change and even then, used as a rough guide. 

== Why are my TIF raster outputs not updated after a model rerun? ==
The GeoTIFF raster output is supported from the 2023-03 Release and onwards. It is considered an image by File Explorer and for some users, the 'Date' in file explorer defaults to show the 'Date Created' rather than the 'Date Modified'. If it appears that your results in the 'Results' folder (e.g. XMDF) are being updated however the results in the 'Results/grid' folder (e.g. TIF) aren't, this may be the reason. 
To confirm, turn on both 'Date Created' and 'Date Modified': 
[[File:TIF Date 01.png | 500px]] 
The 'Date Modified' column will show the date of the last model run: 
[[File:TIF Date 02.png | 500px]] 
 

== How do I apply high resolution (HR) output and what are the options? ==
See the [[TUFLOW_HR_Output | TUFLOW HR Output]] Wiki page for details. 

== Why are there differences between high resolution grid from TUFLOW and remapped outputs generated by the asc_to_asc utility? ==
Differences are expected between the direct HR output and the [[ASC_to_ASC#Remap | remap function]] in the asc_to_asc utility due to the different steps involved in the interpolation of water level results. 
The direct HR output is based on a triangulation of the water levels at the cell centres and corners. In comparison, the remap function involves two interpolation steps. The first is the interpolation from the cell centres and corners to the grid map output locations (by default, half the cell size). Then, the asc_to_asc utility interpolates the water level output to the target DEM grids with fine resolution: 
[[File:High-res-interpolation.jpg|600px]] 

The direct HR output tends to have better resolution in comparison to the remapped results. Due to the additional stages of interpolation involved with the remapped output, there is potential to lose definition in the results particularly in areas with steep slopes.

==How to create a maximum grid for datasets without direct maximum output from a TUFLOW run?==

For cumulative datasets without direct maximum output (e.g., cumulative infiltration, cumulative rainfall etc), the map output interval in the TCF needs to match the simulation end time in seconds. The end time output is when the maximum occurs for cumulative datasets, as it represents the cumulative results over the entire simulation duration.

For non-cumulative datasets without direct maximum output (e.g., Froude number, rainfall rate etc):
*Adjust the <tt>Map Output Interval</tt> in the TCF to a small value to ensure temporal variations are captured.
*Specify XMDF for <tt>Map Output Format</tt>.
*Identify the output ID number of the desired dataset within the .xmdf file:
<pre>"C:\TUFLOW\Releases\res_to_res\res_to_res_w64.exe" -xnfo results.xmdf</pre>
*Extract maximum values using the [[RES_to_RES#Introduction | RES to RES]] utility.
*Confirm the dataset's output ID number in the resulting (maxmax).xmdf file:
<pre>"C:\TUFLOW\Releases\res_to_res\res_to_res_w64.exe" -xnfo (maxmax).xmdf</pre>
*Convert the resulting dataset to a preferred grid format for GIS use using the [[TUFLOW_to_GIS#Introduction | TUFLOW to GIS]] utility.

:Note: For non-cumulative datasets, this method does not capture the true maximum, however can be close enough with a fine map output interval.

== Why are the values of the mapped water level results (h_max) lower than the terrain elevations (DEM_Z)? ==

As at TUFLOW Build 25.1.1, there is a current limitation for result outputs at shallow sheet flow cells on steeper terrain in sub-grid-scale (SGS) enabled models. Direct rainfall models are particularly prone to result in substantial extents of shallow sheet flow.

In TUFLOW HPC, the cell averaged depth is used computationally and by default in the depth outputs (see below).

Water level outputs, however, assume that water fills from the bottom of the SGS cell’s storage curve. This water level result is recorded at the cell centre, interpolated to the cell corners, and then the gridded outputs are triangulated from these points (orange line). In sheet flow situations when the volume in that cell is small, the cell-centered water level may sit below the terrain (blue dot on diagram) if the cell is steep enough. This can result in a water level results sitting below the DEM_Z, even when there is a positive depth result. Note this does not affect the simulation computationally, only how the water level results are output. 
[[File:SGS_Sheetflow_Waterlevel.jpg|600px]]

== The modelled water surface level is below the SGS cell elevations. How should this result be interpreted or presented? ==
Below ground WSE in wet SGS cells is a known output behaviour in SGS enabled models and does not indicate an error in the hydraulic solution.

This occurs because TUFLOW reports water level assuming water fills from the lowest part of the SGS cell during output. As a result, cells may be wet while the reported WSE remains below the cell centre ground level.

This behaviour commonly occurs in the following situations:

'''Channel edge partial wetting'''

* Partially wet cells along channel banks may contain water that does not reach the cell centre elevation.
* In this case, compare the standard WSE with the standard DEM_Z check file to confirm consistency with terrain levels.

'''Shallow sheet flow from rainfall or side inflow'''

Cells may be wet but still report WSE below the cell centre ground level because the depth is small and distributed across the SGS terrain.

'''Presentation and reporting considerations'''

* A presentation method is to post process results by adding the cell averaged depth output <tt>SGS Depth Output</tt> <tt>==</tt><tt> CELL AVERAGE</tt> to the DEM elevation to derive an above ground water level.
* This approach can improve the visual representation of shallow flooding in sheet flow areas but may produce unrealistic water levels in fully wet or partially wet cells.
* Conditional IF logic may be required to choose where to present cell averaged depth plus DEM and where to retain the standard TUFLOW water level output.

== Why do the high-resolution (HR) mapped results have a significantly smaller extent than the standard (non-HR) grid outputs for a sub-grid-scale (SGS) direct rainfall HPC model? ==

The high resolution (HR) outputs are based on [[TUFLOW_HR_Output | intersecting]] the standard water level output results with the HR DEM_Z. Due to the [[TUFLOW_Output_Discussion#Why_are_the_values_of_the_mapped_water_level_results_.28h_max.29_lower_than_the_terrain_elevations_.28DEM_Z.29.3F | output limitation]] for sub-grid-scale (SGS) enabled models with shallow sheet flow (particularly direct rainfall models) on sufficiently steep terrain, water levels can be output lower than the DEM_Z terrain elevations. This is due to the assumption (for output purposes) that water fills the SGS cell’s storage curve from the bottom, which differs from the approach of cell-average depth used computationally in TUFLOW HPC and by default in depth outputs. When the HR water level results are processed, the full extent of the water level results are intersected with the HR DEM_Z and any water level results that are lower than the HR DEM_Z elevations due to the above effect are trimmed. The extent of the HR water level results are then used to trim the HR depth outputs.

== When should high-resolution (HR) outputs be used, and what are their limitations? ==
HR outputs are intended for visualisation and presentation because they retain sub-grid terrain detail but do not improve the underlying hydraulic calculations.

They are not suitable for assessment, benchmarking, or detailed analysis. Standard non-HR map outputs should be used for these purposes, and accuracy should be improved by refining the 2D cell size.
{{Tips Navigation
|uplink=[[TUFLOW_Modelling_Guidance | Back to TUFLOW Modelling Guidance]]
}}

TUFLOW Output Discussion

2026-02-14T07:46:56Z

Abrar.Alttahir: /* How should below ground water surface elevation (WSE) in wet SGS cells be interpreted or presented? */

= Common Questions Answered (FAQ)=
== Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)? ==
Slight differences are expected between the result grids (ASC or FLT) and mesh results (XMDF or DAT) due to small differences in the interpolation method.
* When outputting directly to FLT or ASC using the <tt>Map Output Format</tt> command, the output grid results are derived from both the cell centres and cell corners and the final results has those values interpolated to the centres of half cells (by default).
* When outputting XMDF (or DAT) format, the mesh results are derived from cell centres for depths / levels and cell sides for vector data. The final XMDF stores data interpolated to the cell corners and doesn’t have any memory of where the corner data were derived from and what those values were.
The differences can also be amplified when the rotation of the model is not perfectly north-south aligned (i.e., a non-zero orientation). 

== Why are grids processed with TUFLOW_to_GIS different from TUFLOW grids? ==
Slight differences are expected between the result grids (ASC or FLT) produced directly from TUFLOW and post-processed from the TUFLOW_to_GIS utility from XMDF or DAT due to differences in the interpolation approach.
Check [[TUFLOW_Output_Discussion#Why_are_grid_outputs_.28ASC.2C_FLT.29_slightly_different_from_mesh_outputs_.28XMDF.2C_DAT.29.3F | Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)?]] question above on differences between outputs direct from the TUFLOW Engine. 
When ASC/FLT results are sampled from the XMDF files with the TUFLOW_to_GIS utility, it uses less data points than when the grids are written directly from TUFLOW.

The differences can also be amplified when the rotation of the model is a non-zero value.
The extent of the grids is also different. When TUFLOW starts it removes any redundant areas (if any) outside of 2d_code layer to reduce the calculation time:
* The extent of grids written directly from TUFLOW works with the unreduced area, encapsulating the entire rotated model domain in a north-south orientated rectangle.
* The extent of XMDF (or DAT) results is based on the reduced area. When the TUFLOW_to_GIS utility is used the post-processed north-south aligned grids encapsulate the reduced model extent. The origin coordinates of such grids doesn't match the origin of the model (model domain), neither the grids written out from TUFLOW directly.

In general, a better interpolation of results is achieved when the grids are written directly from TUFLOW than when post-processed with TUFLOW_to_GIS utility. Importantly, when comparing results the recommendation is to be consistent and to use the same method for the duration of a project to ensure there are no differences as a consequence of the post-processing method. 

== Why is Z0 hazard coming directly from TUFLOW different to the one calculated from maximum velocity and depth? ==
The maximum velocity and maximum depth doesn’t necessarily occur at the same time. The Z0 hazard written directly from TUFLOW has the depth x velocity calculated every timestep and at the end outputs the maximums of all the timesteps. When using the velocity and depth grids the maximum velocity is always used with maximum depth. 
Outputting the Z0 hazard grid directly from TUFLOW is more accurate, however, if this is not an option, manual creation is an acceptable workaround. The general recommendation is to choose one method and keep it consistent for the length of the project. 
When calculating the Z0 hazard from the velocity and depth, the grids have already been interpolated by TUFLOW before the raster calculation (less data is used for the calculation), whereas the TUFLOW Z0 output is interpolated from the calculated data directly. Please also refer to the above question for more information [[TUFLOW_Output_Discussion#Why_are_grid_outputs_.28ASC.2C_FLT.29_slightly_different_from_mesh_outputs_.28XMDF.2C_DAT.29.3F | Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)?]]. 

== Why does hazard output (Z1, Z2, ...) show float values and not just integers? ==
The hazard values are calculated in cell corners and are in a form of an integer. When the values are processed into a grid, which has, by default, a resolution of half the cell size, the values are interpolated to the cell centres - four cell centre values per cell. Further interpolation occurs when the model has a non-zero orientation as grid files are north-south aligned. 

== Why are results from 2d_po features, POMM.csv and plots extracted from XMDF in GIS package not matching? ==
There are numerous reasons for the outputs to not be matching:
* Plot output:
** The 2d_po results are tracked at every <tt>Time Series Output Interval</tt> and saved into the PO.csv file. If the water level peaks in between the intervals, that maximum water level won’t be recorded in the PO.csv.
** The POMM.csv tracks maximums of PO features at every computational timestep. 
* Map output:
** The temporal XMDF (or DAT) results are tracked every <tt>Map Output Interval</tt> and interpolated to the cell corners.
** The maximum XMDF map output records the value at every computational timestep, however the final maximum map output is also being interpolated to the cell corners. 
** The extraction tools in GIS packages use interpolated data for the plot creation, e.g. less data than the TUFLOW direct plot output.
* Although the maximums from POMM.csv and the maximum map output will be very close, they won't match exactly.

Further notes for using post-processed flux line:
* No information is stored in the map output about whether the flow is upstream or downstream controlled.
* The staggered computational grid means there is always some interpolation occurring when projecting levels and velocities to the flux line. This will cause significant uncertainty in areas of sudden topographic change such as at an embankment. It is also the reason that two similar lines side by side may provide different outputs.
* Highly variable/complex flows, which are usually associated with highly variable topography, will add to the uncertainty when interpolating to the flux line.

The general recommendation is to use a lot of 2d_po features with reasonably small <tt>Time Series Output Interval</tt> to achieve better accuracy, they won't slow the simulation down. In case of flux lines, the 2d_po lines are using the actual computed fluxes across each cell face and will be an exact representation of the flow being calculated by the 2D solver. Post-processed fluxes should only be located in areas of little topographic change and even then, used as a rough guide. 

== Why are my TIF raster outputs not updated after a model rerun? ==
The GeoTIFF raster output is supported from the 2023-03 Release and onwards. It is considered an image by File Explorer and for some users, the 'Date' in file explorer defaults to show the 'Date Created' rather than the 'Date Modified'. If it appears that your results in the 'Results' folder (e.g. XMDF) are being updated however the results in the 'Results/grid' folder (e.g. TIF) aren't, this may be the reason. 
To confirm, turn on both 'Date Created' and 'Date Modified': 
[[File:TIF Date 01.png | 500px]] 
The 'Date Modified' column will show the date of the last model run: 
[[File:TIF Date 02.png | 500px]] 
 

== How do I apply high resolution (HR) output and what are the options? ==
See the [[TUFLOW_HR_Output | TUFLOW HR Output]] Wiki page for details. 

== Why are there differences between high resolution grid from TUFLOW and remapped outputs generated by the asc_to_asc utility? ==
Differences are expected between the direct HR output and the [[ASC_to_ASC#Remap | remap function]] in the asc_to_asc utility due to the different steps involved in the interpolation of water level results. 
The direct HR output is based on a triangulation of the water levels at the cell centres and corners. In comparison, the remap function involves two interpolation steps. The first is the interpolation from the cell centres and corners to the grid map output locations (by default, half the cell size). Then, the asc_to_asc utility interpolates the water level output to the target DEM grids with fine resolution: 
[[File:High-res-interpolation.jpg|600px]] 

The direct HR output tends to have better resolution in comparison to the remapped results. Due to the additional stages of interpolation involved with the remapped output, there is potential to lose definition in the results particularly in areas with steep slopes.

==How to create a maximum grid for datasets without direct maximum output from a TUFLOW run?==

For cumulative datasets without direct maximum output (e.g., cumulative infiltration, cumulative rainfall etc), the map output interval in the TCF needs to match the simulation end time in seconds. The end time output is when the maximum occurs for cumulative datasets, as it represents the cumulative results over the entire simulation duration.

For non-cumulative datasets without direct maximum output (e.g., Froude number, rainfall rate etc):
*Adjust the <tt>Map Output Interval</tt> in the TCF to a small value to ensure temporal variations are captured.
*Specify XMDF for <tt>Map Output Format</tt>.
*Identify the output ID number of the desired dataset within the .xmdf file:
<pre>"C:\TUFLOW\Releases\res_to_res\res_to_res_w64.exe" -xnfo results.xmdf</pre>
*Extract maximum values using the [[RES_to_RES#Introduction | RES to RES]] utility.
*Confirm the dataset's output ID number in the resulting (maxmax).xmdf file:
<pre>"C:\TUFLOW\Releases\res_to_res\res_to_res_w64.exe" -xnfo (maxmax).xmdf</pre>
*Convert the resulting dataset to a preferred grid format for GIS use using the [[TUFLOW_to_GIS#Introduction | TUFLOW to GIS]] utility.

:Note: For non-cumulative datasets, this method does not capture the true maximum, however can be close enough with a fine map output interval.

== Why are the values of the mapped water level results (h_max) lower than the terrain elevations (DEM_Z)? ==

As at TUFLOW Build 25.1.1, there is a current limitation for result outputs at shallow sheet flow cells on steeper terrain in sub-grid-scale (SGS) enabled models. Direct rainfall models are particularly prone to result in substantial extents of shallow sheet flow.

In TUFLOW HPC, the cell averaged depth is used computationally and by default in the depth outputs (see below).

Water level outputs, however, assume that water fills from the bottom of the SGS cell’s storage curve. This water level result is recorded at the cell centre, interpolated to the cell corners, and then the gridded outputs are triangulated from these points (orange line). In sheet flow situations when the volume in that cell is small, the cell-centered water level may sit below the terrain (blue dot on diagram) if the cell is steep enough. This can result in a water level results sitting below the DEM_Z, even when there is a positive depth result. Note this does not affect the simulation computationally, only how the water level results are output. 
[[File:SGS_Sheetflow_Waterlevel.jpg|600px]]

== How should below ground water surface elevation (WSE) in wet SGS cells be interpreted or presented? ==
Below ground WSE in wet SGS cells is a known output behaviour in SGS enabled models and does not indicate an error in the hydraulic solution.

This occurs because TUFLOW reports water level assuming water fills from the lowest part of the SGS cell during output. As a result, cells may be wet while the reported WSE remains below the cell centre ground level.

This behaviour commonly occurs in the following situations:

'''Channel edge partial wetting'''

* Partially wet cells along channel banks may contain water that does not reach the cell centre elevation.
* In this case, compare the standard WSE with the standard DEM_Z check file to confirm consistency with terrain levels.

'''Shallow sheet flow from rainfall or side inflow'''

Cells may be wet but still report WSE below the cell centre ground level because the depth is small and distributed across the SGS terrain.

'''Presentation and reporting considerations'''

* A presentation method is to post process results by adding the cell averaged depth output <tt>SGS Depth Output</tt> <tt>==</tt><tt> CELL AVERAGE</tt> to the DEM elevation to derive an above ground water level.
* This approach can improve the visual representation of shallow flooding in sheet flow areas but may produce unrealistic water levels in fully wet or partially wet cells.
* Conditional IF logic may be required to choose where to present cell averaged depth plus DEM and where to retain the standard TUFLOW water level output.

== Why do the high-resolution (HR) mapped results have a significantly smaller extent than the standard (non-HR) grid outputs for a sub-grid-scale (SGS) direct rainfall HPC model? ==

The high resolution (HR) outputs are based on [[TUFLOW_HR_Output | intersecting]] the standard water level output results with the HR DEM_Z. Due to the [[TUFLOW_Output_Discussion#Why_are_the_values_of_the_mapped_water_level_results_.28h_max.29_lower_than_the_terrain_elevations_.28DEM_Z.29.3F | output limitation]] for sub-grid-scale (SGS) enabled models with shallow sheet flow (particularly direct rainfall models) on sufficiently steep terrain, water levels can be output lower than the DEM_Z terrain elevations. This is due to the assumption (for output purposes) that water fills the SGS cell’s storage curve from the bottom, which differs from the approach of cell-average depth used computationally in TUFLOW HPC and by default in depth outputs. When the HR water level results are processed, the full extent of the water level results are intersected with the HR DEM_Z and any water level results that are lower than the HR DEM_Z elevations due to the above effect are trimmed. The extent of the HR water level results are then used to trim the HR depth outputs.

== When should high-resolution (HR) outputs be used, and what are their limitations? ==
HR outputs are intended for visualisation and presentation because they retain sub-grid terrain detail but do not improve the underlying hydraulic calculations.

They are not suitable for assessment, benchmarking, or detailed analysis. Standard non-HR map outputs should be used for these purposes, and accuracy should be improved by refining the 2D cell size.
{{Tips Navigation
|uplink=[[TUFLOW_Modelling_Guidance | Back to TUFLOW Modelling Guidance]]
}}

TUFLOW Output Discussion

2026-02-14T07:44:26Z

Abrar.Alttahir: /* How should below ground water surface elevation (WSE) in wet SGS cells be interpreted or presented? */

= Common Questions Answered (FAQ)=
== Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)? ==
Slight differences are expected between the result grids (ASC or FLT) and mesh results (XMDF or DAT) due to small differences in the interpolation method.
* When outputting directly to FLT or ASC using the <tt>Map Output Format</tt> command, the output grid results are derived from both the cell centres and cell corners and the final results has those values interpolated to the centres of half cells (by default).
* When outputting XMDF (or DAT) format, the mesh results are derived from cell centres for depths / levels and cell sides for vector data. The final XMDF stores data interpolated to the cell corners and doesn’t have any memory of where the corner data were derived from and what those values were.
The differences can also be amplified when the rotation of the model is not perfectly north-south aligned (i.e., a non-zero orientation). 

== Why are grids processed with TUFLOW_to_GIS different from TUFLOW grids? ==
Slight differences are expected between the result grids (ASC or FLT) produced directly from TUFLOW and post-processed from the TUFLOW_to_GIS utility from XMDF or DAT due to differences in the interpolation approach.
Check [[TUFLOW_Output_Discussion#Why_are_grid_outputs_.28ASC.2C_FLT.29_slightly_different_from_mesh_outputs_.28XMDF.2C_DAT.29.3F | Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)?]] question above on differences between outputs direct from the TUFLOW Engine. 
When ASC/FLT results are sampled from the XMDF files with the TUFLOW_to_GIS utility, it uses less data points than when the grids are written directly from TUFLOW.

The differences can also be amplified when the rotation of the model is a non-zero value.
The extent of the grids is also different. When TUFLOW starts it removes any redundant areas (if any) outside of 2d_code layer to reduce the calculation time:
* The extent of grids written directly from TUFLOW works with the unreduced area, encapsulating the entire rotated model domain in a north-south orientated rectangle.
* The extent of XMDF (or DAT) results is based on the reduced area. When the TUFLOW_to_GIS utility is used the post-processed north-south aligned grids encapsulate the reduced model extent. The origin coordinates of such grids doesn't match the origin of the model (model domain), neither the grids written out from TUFLOW directly.

In general, a better interpolation of results is achieved when the grids are written directly from TUFLOW than when post-processed with TUFLOW_to_GIS utility. Importantly, when comparing results the recommendation is to be consistent and to use the same method for the duration of a project to ensure there are no differences as a consequence of the post-processing method. 

== Why is Z0 hazard coming directly from TUFLOW different to the one calculated from maximum velocity and depth? ==
The maximum velocity and maximum depth doesn’t necessarily occur at the same time. The Z0 hazard written directly from TUFLOW has the depth x velocity calculated every timestep and at the end outputs the maximums of all the timesteps. When using the velocity and depth grids the maximum velocity is always used with maximum depth. 
Outputting the Z0 hazard grid directly from TUFLOW is more accurate, however, if this is not an option, manual creation is an acceptable workaround. The general recommendation is to choose one method and keep it consistent for the length of the project. 
When calculating the Z0 hazard from the velocity and depth, the grids have already been interpolated by TUFLOW before the raster calculation (less data is used for the calculation), whereas the TUFLOW Z0 output is interpolated from the calculated data directly. Please also refer to the above question for more information [[TUFLOW_Output_Discussion#Why_are_grid_outputs_.28ASC.2C_FLT.29_slightly_different_from_mesh_outputs_.28XMDF.2C_DAT.29.3F | Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)?]]. 

== Why does hazard output (Z1, Z2, ...) show float values and not just integers? ==
The hazard values are calculated in cell corners and are in a form of an integer. When the values are processed into a grid, which has, by default, a resolution of half the cell size, the values are interpolated to the cell centres - four cell centre values per cell. Further interpolation occurs when the model has a non-zero orientation as grid files are north-south aligned. 

== Why are results from 2d_po features, POMM.csv and plots extracted from XMDF in GIS package not matching? ==
There are numerous reasons for the outputs to not be matching:
* Plot output:
** The 2d_po results are tracked at every <tt>Time Series Output Interval</tt> and saved into the PO.csv file. If the water level peaks in between the intervals, that maximum water level won’t be recorded in the PO.csv.
** The POMM.csv tracks maximums of PO features at every computational timestep. 
* Map output:
** The temporal XMDF (or DAT) results are tracked every <tt>Map Output Interval</tt> and interpolated to the cell corners.
** The maximum XMDF map output records the value at every computational timestep, however the final maximum map output is also being interpolated to the cell corners. 
** The extraction tools in GIS packages use interpolated data for the plot creation, e.g. less data than the TUFLOW direct plot output.
* Although the maximums from POMM.csv and the maximum map output will be very close, they won't match exactly.

Further notes for using post-processed flux line:
* No information is stored in the map output about whether the flow is upstream or downstream controlled.
* The staggered computational grid means there is always some interpolation occurring when projecting levels and velocities to the flux line. This will cause significant uncertainty in areas of sudden topographic change such as at an embankment. It is also the reason that two similar lines side by side may provide different outputs.
* Highly variable/complex flows, which are usually associated with highly variable topography, will add to the uncertainty when interpolating to the flux line.

The general recommendation is to use a lot of 2d_po features with reasonably small <tt>Time Series Output Interval</tt> to achieve better accuracy, they won't slow the simulation down. In case of flux lines, the 2d_po lines are using the actual computed fluxes across each cell face and will be an exact representation of the flow being calculated by the 2D solver. Post-processed fluxes should only be located in areas of little topographic change and even then, used as a rough guide. 

== Why are my TIF raster outputs not updated after a model rerun? ==
The GeoTIFF raster output is supported from the 2023-03 Release and onwards. It is considered an image by File Explorer and for some users, the 'Date' in file explorer defaults to show the 'Date Created' rather than the 'Date Modified'. If it appears that your results in the 'Results' folder (e.g. XMDF) are being updated however the results in the 'Results/grid' folder (e.g. TIF) aren't, this may be the reason. 
To confirm, turn on both 'Date Created' and 'Date Modified': 
[[File:TIF Date 01.png | 500px]] 
The 'Date Modified' column will show the date of the last model run: 
[[File:TIF Date 02.png | 500px]] 
 

== How do I apply high resolution (HR) output and what are the options? ==
See the [[TUFLOW_HR_Output | TUFLOW HR Output]] Wiki page for details. 

== Why are there differences between high resolution grid from TUFLOW and remapped outputs generated by the asc_to_asc utility? ==
Differences are expected between the direct HR output and the [[ASC_to_ASC#Remap | remap function]] in the asc_to_asc utility due to the different steps involved in the interpolation of water level results. 
The direct HR output is based on a triangulation of the water levels at the cell centres and corners. In comparison, the remap function involves two interpolation steps. The first is the interpolation from the cell centres and corners to the grid map output locations (by default, half the cell size). Then, the asc_to_asc utility interpolates the water level output to the target DEM grids with fine resolution: 
[[File:High-res-interpolation.jpg|600px]] 

The direct HR output tends to have better resolution in comparison to the remapped results. Due to the additional stages of interpolation involved with the remapped output, there is potential to lose definition in the results particularly in areas with steep slopes.

==How to create a maximum grid for datasets without direct maximum output from a TUFLOW run?==

For cumulative datasets without direct maximum output (e.g., cumulative infiltration, cumulative rainfall etc), the map output interval in the TCF needs to match the simulation end time in seconds. The end time output is when the maximum occurs for cumulative datasets, as it represents the cumulative results over the entire simulation duration.

For non-cumulative datasets without direct maximum output (e.g., Froude number, rainfall rate etc):
*Adjust the <tt>Map Output Interval</tt> in the TCF to a small value to ensure temporal variations are captured.
*Specify XMDF for <tt>Map Output Format</tt>.
*Identify the output ID number of the desired dataset within the .xmdf file:
<pre>"C:\TUFLOW\Releases\res_to_res\res_to_res_w64.exe" -xnfo results.xmdf</pre>
*Extract maximum values using the [[RES_to_RES#Introduction | RES to RES]] utility.
*Confirm the dataset's output ID number in the resulting (maxmax).xmdf file:
<pre>"C:\TUFLOW\Releases\res_to_res\res_to_res_w64.exe" -xnfo (maxmax).xmdf</pre>
*Convert the resulting dataset to a preferred grid format for GIS use using the [[TUFLOW_to_GIS#Introduction | TUFLOW to GIS]] utility.

:Note: For non-cumulative datasets, this method does not capture the true maximum, however can be close enough with a fine map output interval.

== Why are the values of the mapped water level results (h_max) lower than the terrain elevations (DEM_Z)? ==

As at TUFLOW Build 25.1.1, there is a current limitation for result outputs at shallow sheet flow cells on steeper terrain in sub-grid-scale (SGS) enabled models. Direct rainfall models are particularly prone to result in substantial extents of shallow sheet flow.

In TUFLOW HPC, the cell averaged depth is used computationally and by default in the depth outputs (see below).

Water level outputs, however, assume that water fills from the bottom of the SGS cell’s storage curve. This water level result is recorded at the cell centre, interpolated to the cell corners, and then the gridded outputs are triangulated from these points (orange line). In sheet flow situations when the volume in that cell is small, the cell-centered water level may sit below the terrain (blue dot on diagram) if the cell is steep enough. This can result in a water level results sitting below the DEM_Z, even when there is a positive depth result. Note this does not affect the simulation computationally, only how the water level results are output. 
[[File:SGS_Sheetflow_Waterlevel.jpg|600px]]

== How should below ground water surface elevation (WSE) in wet SGS cells be interpreted or presented? ==
Below ground WSE in wet SGS cells is a known output behaviour in SGS enabled models and does not indicate an error in the hydraulic solution.

This occurs because TUFLOW reports water level assuming water fills from the lowest part of the SGS cell during output. As a result, cells may be wet while the reported water surface elevation remains below the cell centre ground level.

This behaviour commonly occurs in the following situations:

'''Channel edge partial wetting'''

* Partially wet cells along channel banks may contain water that does not reach the cell centre elevation.
* In this case, compare the standard water surface elevation with the standard DEM Z check file to confirm consistency with terrain levels.

'''Shallow sheet flow from rainfall or side inflow'''

Cells may be wet but still report water surface elevation below the cell centre ground level because the depth is small and distributed across the SGS terrain.

'''Presentation and reporting considerations'''

* A presentation method is to post process results by adding the cell averaged depth output <tt>SGS Depth Output</tt> <tt>==</tt><tt> CELL AVERAGE</tt> to the DEM elevation to derive an above ground water level.
* This approach can improve the visual representation of shallow flooding in sheet flow areas but may produce unrealistic water levels in fully wet or partially wet cells.
* Conditional IF logic may be required to choose where to present cell averaged depth plus DEM and where to retain the standard TUFLOW water level output.

== Why do the high-resolution (HR) mapped results have a significantly smaller extent than the standard (non-HR) grid outputs for a sub-grid-scale (SGS) direct rainfall HPC model? ==

The high resolution (HR) outputs are based on [[TUFLOW_HR_Output | intersecting]] the standard water level output results with the HR DEM_Z. Due to the [[TUFLOW_Output_Discussion#Why_are_the_values_of_the_mapped_water_level_results_.28h_max.29_lower_than_the_terrain_elevations_.28DEM_Z.29.3F | output limitation]] for sub-grid-scale (SGS) enabled models with shallow sheet flow (particularly direct rainfall models) on sufficiently steep terrain, water levels can be output lower than the DEM_Z terrain elevations. This is due to the assumption (for output purposes) that water fills the SGS cell’s storage curve from the bottom, which differs from the approach of cell-average depth used computationally in TUFLOW HPC and by default in depth outputs. When the HR water level results are processed, the full extent of the water level results are intersected with the HR DEM_Z and any water level results that are lower than the HR DEM_Z elevations due to the above effect are trimmed. The extent of the HR water level results are then used to trim the HR depth outputs.

== When should high-resolution (HR) outputs be used, and what are their limitations? ==
HR outputs are intended for visualisation and presentation because they retain sub-grid terrain detail but do not improve the underlying hydraulic calculations.

They are not suitable for assessment, benchmarking, or detailed analysis. Standard non-HR map outputs should be used for these purposes, and accuracy should be improved by refining the 2D cell size.
{{Tips Navigation
|uplink=[[TUFLOW_Modelling_Guidance | Back to TUFLOW Modelling Guidance]]
}}

TUFLOW Output Discussion

2026-02-14T07:38:04Z

Abrar.Alttahir:

= Common Questions Answered (FAQ)=
== Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)? ==
Slight differences are expected between the result grids (ASC or FLT) and mesh results (XMDF or DAT) due to small differences in the interpolation method.
* When outputting directly to FLT or ASC using the <tt>Map Output Format</tt> command, the output grid results are derived from both the cell centres and cell corners and the final results has those values interpolated to the centres of half cells (by default).
* When outputting XMDF (or DAT) format, the mesh results are derived from cell centres for depths / levels and cell sides for vector data. The final XMDF stores data interpolated to the cell corners and doesn’t have any memory of where the corner data were derived from and what those values were.
The differences can also be amplified when the rotation of the model is not perfectly north-south aligned (i.e., a non-zero orientation). 

== Why are grids processed with TUFLOW_to_GIS different from TUFLOW grids? ==
Slight differences are expected between the result grids (ASC or FLT) produced directly from TUFLOW and post-processed from the TUFLOW_to_GIS utility from XMDF or DAT due to differences in the interpolation approach.
Check [[TUFLOW_Output_Discussion#Why_are_grid_outputs_.28ASC.2C_FLT.29_slightly_different_from_mesh_outputs_.28XMDF.2C_DAT.29.3F | Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)?]] question above on differences between outputs direct from the TUFLOW Engine. 
When ASC/FLT results are sampled from the XMDF files with the TUFLOW_to_GIS utility, it uses less data points than when the grids are written directly from TUFLOW.

The differences can also be amplified when the rotation of the model is a non-zero value.
The extent of the grids is also different. When TUFLOW starts it removes any redundant areas (if any) outside of 2d_code layer to reduce the calculation time:
* The extent of grids written directly from TUFLOW works with the unreduced area, encapsulating the entire rotated model domain in a north-south orientated rectangle.
* The extent of XMDF (or DAT) results is based on the reduced area. When the TUFLOW_to_GIS utility is used the post-processed north-south aligned grids encapsulate the reduced model extent. The origin coordinates of such grids doesn't match the origin of the model (model domain), neither the grids written out from TUFLOW directly.

In general, a better interpolation of results is achieved when the grids are written directly from TUFLOW than when post-processed with TUFLOW_to_GIS utility. Importantly, when comparing results the recommendation is to be consistent and to use the same method for the duration of a project to ensure there are no differences as a consequence of the post-processing method. 

== Why is Z0 hazard coming directly from TUFLOW different to the one calculated from maximum velocity and depth? ==
The maximum velocity and maximum depth doesn’t necessarily occur at the same time. The Z0 hazard written directly from TUFLOW has the depth x velocity calculated every timestep and at the end outputs the maximums of all the timesteps. When using the velocity and depth grids the maximum velocity is always used with maximum depth. 
Outputting the Z0 hazard grid directly from TUFLOW is more accurate, however, if this is not an option, manual creation is an acceptable workaround. The general recommendation is to choose one method and keep it consistent for the length of the project. 
When calculating the Z0 hazard from the velocity and depth, the grids have already been interpolated by TUFLOW before the raster calculation (less data is used for the calculation), whereas the TUFLOW Z0 output is interpolated from the calculated data directly. Please also refer to the above question for more information [[TUFLOW_Output_Discussion#Why_are_grid_outputs_.28ASC.2C_FLT.29_slightly_different_from_mesh_outputs_.28XMDF.2C_DAT.29.3F | Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)?]]. 

== Why does hazard output (Z1, Z2, ...) show float values and not just integers? ==
The hazard values are calculated in cell corners and are in a form of an integer. When the values are processed into a grid, which has, by default, a resolution of half the cell size, the values are interpolated to the cell centres - four cell centre values per cell. Further interpolation occurs when the model has a non-zero orientation as grid files are north-south aligned. 

== Why are results from 2d_po features, POMM.csv and plots extracted from XMDF in GIS package not matching? ==
There are numerous reasons for the outputs to not be matching:
* Plot output:
** The 2d_po results are tracked at every <tt>Time Series Output Interval</tt> and saved into the PO.csv file. If the water level peaks in between the intervals, that maximum water level won’t be recorded in the PO.csv.
** The POMM.csv tracks maximums of PO features at every computational timestep. 
* Map output:
** The temporal XMDF (or DAT) results are tracked every <tt>Map Output Interval</tt> and interpolated to the cell corners.
** The maximum XMDF map output records the value at every computational timestep, however the final maximum map output is also being interpolated to the cell corners. 
** The extraction tools in GIS packages use interpolated data for the plot creation, e.g. less data than the TUFLOW direct plot output.
* Although the maximums from POMM.csv and the maximum map output will be very close, they won't match exactly.

Further notes for using post-processed flux line:
* No information is stored in the map output about whether the flow is upstream or downstream controlled.
* The staggered computational grid means there is always some interpolation occurring when projecting levels and velocities to the flux line. This will cause significant uncertainty in areas of sudden topographic change such as at an embankment. It is also the reason that two similar lines side by side may provide different outputs.
* Highly variable/complex flows, which are usually associated with highly variable topography, will add to the uncertainty when interpolating to the flux line.

The general recommendation is to use a lot of 2d_po features with reasonably small <tt>Time Series Output Interval</tt> to achieve better accuracy, they won't slow the simulation down. In case of flux lines, the 2d_po lines are using the actual computed fluxes across each cell face and will be an exact representation of the flow being calculated by the 2D solver. Post-processed fluxes should only be located in areas of little topographic change and even then, used as a rough guide. 

== Why are my TIF raster outputs not updated after a model rerun? ==
The GeoTIFF raster output is supported from the 2023-03 Release and onwards. It is considered an image by File Explorer and for some users, the 'Date' in file explorer defaults to show the 'Date Created' rather than the 'Date Modified'. If it appears that your results in the 'Results' folder (e.g. XMDF) are being updated however the results in the 'Results/grid' folder (e.g. TIF) aren't, this may be the reason. 
To confirm, turn on both 'Date Created' and 'Date Modified': 
[[File:TIF Date 01.png | 500px]] 
The 'Date Modified' column will show the date of the last model run: 
[[File:TIF Date 02.png | 500px]] 
 

== How do I apply high resolution (HR) output and what are the options? ==
See the [[TUFLOW_HR_Output | TUFLOW HR Output]] Wiki page for details. 

== Why are there differences between high resolution grid from TUFLOW and remapped outputs generated by the asc_to_asc utility? ==
Differences are expected between the direct HR output and the [[ASC_to_ASC#Remap | remap function]] in the asc_to_asc utility due to the different steps involved in the interpolation of water level results. 
The direct HR output is based on a triangulation of the water levels at the cell centres and corners. In comparison, the remap function involves two interpolation steps. The first is the interpolation from the cell centres and corners to the grid map output locations (by default, half the cell size). Then, the asc_to_asc utility interpolates the water level output to the target DEM grids with fine resolution: 
[[File:High-res-interpolation.jpg|600px]] 

The direct HR output tends to have better resolution in comparison to the remapped results. Due to the additional stages of interpolation involved with the remapped output, there is potential to lose definition in the results particularly in areas with steep slopes.

==How to create a maximum grid for datasets without direct maximum output from a TUFLOW run?==

For cumulative datasets without direct maximum output (e.g., cumulative infiltration, cumulative rainfall etc), the map output interval in the TCF needs to match the simulation end time in seconds. The end time output is when the maximum occurs for cumulative datasets, as it represents the cumulative results over the entire simulation duration.

For non-cumulative datasets without direct maximum output (e.g., Froude number, rainfall rate etc):
*Adjust the <tt>Map Output Interval</tt> in the TCF to a small value to ensure temporal variations are captured.
*Specify XMDF for <tt>Map Output Format</tt>.
*Identify the output ID number of the desired dataset within the .xmdf file:
<pre>"C:\TUFLOW\Releases\res_to_res\res_to_res_w64.exe" -xnfo results.xmdf</pre>
*Extract maximum values using the [[RES_to_RES#Introduction | RES to RES]] utility.
*Confirm the dataset's output ID number in the resulting (maxmax).xmdf file:
<pre>"C:\TUFLOW\Releases\res_to_res\res_to_res_w64.exe" -xnfo (maxmax).xmdf</pre>
*Convert the resulting dataset to a preferred grid format for GIS use using the [[TUFLOW_to_GIS#Introduction | TUFLOW to GIS]] utility.

:Note: For non-cumulative datasets, this method does not capture the true maximum, however can be close enough with a fine map output interval.

== Why are the values of the mapped water level results (h_max) lower than the terrain elevations (DEM_Z)? ==

As at TUFLOW Build 25.1.1, there is a current limitation for result outputs at shallow sheet flow cells on steeper terrain in sub-grid-scale (SGS) enabled models. Direct rainfall models are particularly prone to result in substantial extents of shallow sheet flow.

In TUFLOW HPC, the cell averaged depth is used computationally and by default in the depth outputs (see below).

Water level outputs, however, assume that water fills from the bottom of the SGS cell’s storage curve. This water level result is recorded at the cell centre, interpolated to the cell corners, and then the gridded outputs are triangulated from these points (orange line). In sheet flow situations when the volume in that cell is small, the cell-centered water level may sit below the terrain (blue dot on diagram) if the cell is steep enough. This can result in a water level results sitting below the DEM_Z, even when there is a positive depth result. Note this does not affect the simulation computationally, only how the water level results are output. 
[[File:SGS_Sheetflow_Waterlevel.jpg|600px]]

== How should below ground water surface elevation (WSE) in wet SGS cells be interpreted or presented? ==
Below ground WSE in wet SGS cells is a known output behaviour in SGS enabled models and does not indicate an error in the hydraulic solution.

This occurs because TUFLOW reports water level assuming water fills from the lowest part of the SGS cell during output. As a result, cells may be wet while the reported water surface elevation remains below the cell centre ground level.

This behaviour commonly occurs in the following situations:

'''Channel edge partial wetting'''

* Partially wet cells along channel banks may contain water that does not reach the cell centre elevation.
* In this case, compare the standard water surface elevation with the standard DEM Z check file to confirm consistency with terrain levels.

'''Shallow sheet flow from rainfall or side inflow'''

Cells may be wet but still report water surface elevation below the cell centre ground level because the depth is small and distributed across the SGS terrain.

'''Presentation and reporting considerations'''

* A presentation method is to post process results by adding the cell averaged depth output <tt>SGS Depth Output == CELL AVERAGE</tt> to the DEM elevation to derive an above ground water level.
* This approach can improve the visual representation of shallow flooding in sheet flow areas but may produce unrealistic water levels in fully wet or partially wet cells.
* Conditional logic may be required to determine where the standard water surface elevation or a derived depth plus DEM level should be presented based on the local flow condition.

== Why do the high-resolution (HR) mapped results have a significantly smaller extent than the standard (non-HR) grid outputs for a sub-grid-scale (SGS) direct rainfall HPC model? ==

The high resolution (HR) outputs are based on [[TUFLOW_HR_Output | intersecting]] the standard water level output results with the HR DEM_Z. Due to the [[TUFLOW_Output_Discussion#Why_are_the_values_of_the_mapped_water_level_results_.28h_max.29_lower_than_the_terrain_elevations_.28DEM_Z.29.3F | output limitation]] for sub-grid-scale (SGS) enabled models with shallow sheet flow (particularly direct rainfall models) on sufficiently steep terrain, water levels can be output lower than the DEM_Z terrain elevations. This is due to the assumption (for output purposes) that water fills the SGS cell’s storage curve from the bottom, which differs from the approach of cell-average depth used computationally in TUFLOW HPC and by default in depth outputs. When the HR water level results are processed, the full extent of the water level results are intersected with the HR DEM_Z and any water level results that are lower than the HR DEM_Z elevations due to the above effect are trimmed. The extent of the HR water level results are then used to trim the HR depth outputs.

== When should high-resolution (HR) outputs be used, and what are their limitations? ==
HR outputs are intended for visualisation and presentation because they retain sub-grid terrain detail but do not improve the underlying hydraulic calculations.

They are not suitable for assessment, benchmarking, or detailed analysis. Standard non-HR map outputs should be used for these purposes, and accuracy should be improved by refining the 2D cell size.
{{Tips Navigation
|uplink=[[TUFLOW_Modelling_Guidance | Back to TUFLOW Modelling Guidance]]
}}

Hardware Benchmarking - Results

2026-02-14T05:00:08Z

Abrar.Alttahir: /* High End GPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"

! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | #
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|1
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||128||5200||32.7||108.6||141.3||JB1
|-
|2
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||64||5600||36.7||114.2||150.9||TM1
|-
|3
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs), ~3.0GHz
|3
|32
|4800
|41.9
|122.4
|164.3
|RA1
|-
|4
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||2.0||128||4000||41.2||124.1||165.3||AW1
|-
|5
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs)||3.2||128||4800||38.5||127.0||165.5||SB1
|-
|6
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz||4.5||64||4800||33.4||136.0||169.4||PSM
|-
|7
|12th Gen Intel(R) Core(TM) i9-12900K||3.2||128||3200||40.7||129.9||170.6||DD2
|-
|8
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz||2.1||64||4800||41||137.2||178.2||RB1
|-
|9
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs) @ ~4.2GHz||4.2||32||4800||35.5||162.8||198.3||LC1
|-
|10
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs)||2.5||16||3200||48.4||170.5||218.9||TS1
|-
|11
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz||3.2||64||4800||53.3||169.1||222.4||CHR
|-
|12
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|13
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|14
|AMD Ryzen 5 7600X 6-Core Processor||4.7||32||4800||39.0||175.7||214.7||HP1
|-
|15
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|16
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|17
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|18
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|19
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||3.6||64||2133||62.7||191.1||253.8||PA1
|-
|20
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|21
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|22
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|23
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|3.2
|450
|
|50.0
|211.6
|261.6
|YW1
|-
|24
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|25
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|26
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||3.2||56||N/A||49.1||218.9||268||RBR
|-
|27
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|28
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|29
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|30
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|31
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|32
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|33
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|34
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||32||2113||68.2||217.9||286.1||HP2
|-
|35
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|36
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|37
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|38
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|39
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|40
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||3.5||128||2666||66.3||234.7||301||JG3
|-
|41
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|42
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|43
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|44
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|45
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|2.6
|32
|3200
|66.6
|243.3
|309.9
|SK1
|-
|46
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|47
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|48
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|49
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz||1.4||16||7467||173.8||152.8||326.6||AL1
|-
|50
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||3.7||64||2933||95.4||231.3||326.7||JB2
|-
|51
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|52
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|53
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|54
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|55
|AMD EPYC 7V12 64-Core Processor (16 CPUs) ||2.4||57||NA||63.5||277.6||341.1||CS2
|-
|56
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz (12 CPUs), ~3.7GHz
|3.7
|32
|2666
|91.45
|259.63
|351.1
|SS1
|-
|57
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|58
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|59
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|60
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|61
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||3.8||64||3200||87.8||294.2||382||HDR-A2000
|-
|62
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|63
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|64
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|65
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|66
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|67
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|68
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|69
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz ||3.0||4||986||84.0||351.4||435.4||TJS
|-
|70
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||3.5||16||1333||125.9||320.5||446.4||EFC
|-
|71
|Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz||1.9||16||1800||100.1||347.0||447.1||SM1
|-
|72
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|73
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||4.1||16||4104||62.7||386.6||449.3||GZH
|-
|74
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||2.4||128||2394||130.5||331.5||462||VDI
|-
|75
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|76
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|77
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|78
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|79
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|80
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|81
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|}

=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | #
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|1
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|2.5
|9.5
|12.0
|JW3
|-
|2
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz ||NVIDIA GeForce RTX 4090||24||16384||2.4||11.6||14.0||JW1
|-
|3
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4090||24||16384||2.5||12.1||14.6||JB1
|-
|4
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz ||NVIDIA GeForce RTX 4090||24||16384||2.7||12.7||15.4||TA1
|-
|5
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||2.9||12.7||15.6||PSM
|-
|6
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||3.1||13.3||16.4||CR4
|-
|7
|Intel(R) Core(TM) i9-14900K (32 CPUs) @ ~3.2GHz||NVIDIA GeForce RTX 4080 Super||16||10240||2.8||14.5||17.3||DF2
|-
|8
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||TM1
|-
|9
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||JM3
|-
|10
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||NVIDIA GeForce RTX 4080||16||9728||3.2||15.0||18.2||AW1
|-
|11
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|3.2
|15.0
|18.2
|TM2
|-
|12
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs)@ ~4.2GHz||NVIDIA GeForce RTX 4080 SUPER||16||10240||3.2||15.7||18.9||LC1
|-
|13
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||NVIDIA RTX 6000 Ada Generation||48||18176||4.9||14.2||19.1||JG3
|-
|14
|Intel(R) Xeon(R) CPU E5-2697 v4 @ 2.30GHz (72 CPUs)||NVIDIA GeForce RTX 4090||24||16384||3.9||16.2||20.1||CS3
|-
|15
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|16
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||3.4||19.2||22.6||HP1
|-
|17
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|3.9
|19.07
|22.97
|PW1
|-
|18
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||3.8||19.2||23.0||RS1
|-
|19
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||3.7||19.4||23.1||DD2
|-
|20
|12th Gen Intel(R) Core(TM) i7-12700F
|NVIDIA GeForce RTX 3080 Ti
|12
|10240
|3.75
|19.57
|23.32
|AUBNE1PC6602
|-
|21
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||4.0||19.6||23.6||GP1
|-
|22
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA A40||48||10752||3.5||20.3||23.8||Lenovo SR650 V2
|-
|23
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.1||20.1||24.2||JMM
|-
|24
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|25
|Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz (36 CPUs), ~3.0GHz
|NVIDIA RTX A6000
|48
|10752
|4.7
|22.7
|27.4
|CB2
|-
|26
|13th Gen Intel(R) Core(TM) i5-13600HX (20 CPUs) @ ~2.6GHz ||NVIDIA GeForce RTX 4090 Laptop GPU||16||9728||5.3||22.2||27.5||LC2
|-
|27
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz ||NVIDIA GeForce RTX 3080||10||8704||4.3||23.7||28||RB1
|-
|28
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|29
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs) ||NVIDIA RTX A5000||24||8192||4.4||24.1||28.5||SB1
|-
|30
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|31
|Intel(R) Xeon(R) W-2223 CPU @ 3.60GHz ||NVIDIA GeForce RTX 3090||24||10496||5.3||24.3||29.6||SSM2
|-
|32
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|33
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|34
|AMD Ryzen Threadripper 2920X 12-Core Processor (24 CPUs) @ 3.5GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.1||25.9||31||CR5
|-
|35
|Intel(R) Xeon(R) W-2145 CPU @ 3.70GHz (16 CPUs), ~3.7GHz
|NVIDIA RTX A5000
|24
|8192
|5.2
|26.3
|31.5
|CB3
|-
|36
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|37
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|38
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|39
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|40
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|41
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|42
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.9||28.4||34.3||HP2
|-
|43
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|44
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|45
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|46
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|47
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|48
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|49
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|50
|Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz ||NVIDIA GRID RTX8000P-48Q||48||4608||6.8||31.0||37.8||VJ1
|-
|51
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs) ||NVIDIA RTX A4000||16||6144||5.8||32.0||37.8||JS2
|-
|52
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|53
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||6.0||32.0||38.0||EFC
|-
|54
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|7.2
|31.4
|38.6
|YW1
|-
|55
|AMD EPYC 74F3 24-Core Processor (36 CPUs) @ 3.2GHz ||NVIDIA A10-24Q||24||8192||7.2||31.4||38.6||CS1
|-
|56
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||33.3||38.7||PA1
|-
|57
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce RTX 3060 Ti (LHR) ||8||4864||5.7||33.3||39.0||DF1
|-
|58
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||NVIDIA GeForce RTX 2060 SUPER (core 1647MHz, mem 1750MHz)||8||2176||5.6||33.4||39.1||GZH
|-
|59
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|60
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|61
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||35.1||41.6||JB2
|-
|62
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|63
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|64
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz ||NVIDIA RTX A4000||16||6144||7.2||35.3||42.5||CHR
|-
|65
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|66
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|67
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|68
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|69
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|70
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|71
|AMD Ryzen 7 7840HS ||NVIDIA GeForce RTX 4060 Laptop GPU||8||3072||7.0||40.6||47.6||CH1
|-
|72
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|73
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||NVIDIA GRID RTX6000P-4Q||24||4608||10.5||39.3||49.8||VDI
|-
|74
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|75
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA Tesla T4||16||2560||6.8||45.2||52.0||RJ1
|-
|76
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz
|NVIDIA GeForce RTX 3060
|8
|3584
|7.68
|45.92
|53.6
|AUBNEW1DQ54G3
|-
|77
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|78
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|79
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|80
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|81
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz (12 CPUs), ~3.7GHz
|NVIDIA GeForce GTX 1080
|8
|2560
|9.13
|52.13
|61.3
|SS1
|-
|82
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|83
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||NVIDIA RTX A2000||6||3328||9.1||54.4||63.5||HDR-A2000
|-
|84
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|85
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz ||NVIDIA GeForce RTX 4050 Laptop GPU||6||2560||7.9||59.4||67.3||AL1
|-
|86
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs), ~3.0GHz
|NVIDIA GeForce RTX A2000
|12
|3328
|8.6
|59.5
|68.1
|RA1
|-
|87
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|88
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA GeForce GTX 1660||6||1408||9.4||61.0||70.4||JFP
|-
|89
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|90
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce GTX 1650 SUPER||4||1280||10.3||64.6||74.9||TS1
|-
|91
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|92
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|93
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|NVIDIA RTX A2000 Laptop GPU
|4
|2560
|11.4
|72.6
|84.0
|SK1
|-
|94
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|95
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|96
|Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz (64 CPUs), ~2.3GHz||NVIDIA Tesla M60||16||4096||12||82.7||94.7||SM2
|-
|97
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|98
|Intel(R) Core(TM) i3-8100 CPU @ 3.60GHz ||NVIDIA Tesla K40c||12||2880||12.3||83.1||95.3||NWE
|-
|99
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA Quadro T2000||4||1024||13.3||85.2||95.5||SSM
|-
|100
|Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz (Laptop) ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|101
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs), ~3.8GHz
|NVIDIA Quadro T1000
|4
|896
|13.6
|91.3
|104.9
|CB4
|-
|102
|13th Gen Intel(R) Core(TM) i7-13800H (20 CPUs), ~2.5GHz
|NVIDIA RTX A500 Laptop GPU
|4
|2048
|14.8
|93.9
|108.7
|YB1
|-
|103
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|104
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|105
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|106
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|107
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|108
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|109
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|110
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|111
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|112
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|113
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz||NVIDIA T500||4||986||29.3||197.5||226.8||TJS
|-
|114
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|115
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|116
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||NVIDIA A10-4Q||24||8192||44.5||212.6||257.1||RBR
|-
|117
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|118
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|119
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|120
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|}

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | #
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|1

|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|73.3
|568.7
|642.0
|JW3
|-
|2
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||NVIDIA GeForce RTX 4090||24||16384||103.3||811.2||914.5||JB1
|-
|3
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz||NVIDIA GeForce RTX 4090||24||16384||105.9||813.7||919.6||TA1
|-
|4
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz||NVIDIA GeForce RTX 4090||24||16384||103.7||824.3||928.0||JW1
|-
|5
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||108.2||821.5||929.7||PSM
|-
|6
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||106.8||875.5||982.3||CR4
|-
|7
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz ||NVIDIA RTX 6000 Ada Generation||48||18176||119.6||877.8||997.4||JG3
|-
|8
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|134.0
|1049.9
|1183.9
|TM2
|-
|9
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||139.5||1115.1||1254.6||JM3
|-
|10
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|11
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||155.5||1176.2||1331.7||JMM
|-
|12
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|13
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|14
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||159.7||1216.2||1375.9||GP1
|-
|15
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||160.9||1240||1400.9||DD2
|-
|16
|Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz ||NVIDIA GeForce RTX 3090||24||10496||172.9||1249.8||1422.7||SIP
|-
|17
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|172.5
|1343.4
|15.15.9
|PW1
|-
|18
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|19
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||190.2||1493.2||1683.4||HP1
|-
|20
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|21
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|22
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|23
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|24
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||215.9||1678.7||1894.6||PA2
|-
|25
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||255.4||1691.7||1947.1||HP2
|-
|26
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|242.3
|1856.5
|2098.8
|YW1
|-
|27
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|28
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|29
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|30
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|31
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|32
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|33
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|34
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|35
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|36
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|37
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|38
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|39
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking | Back to TUFLOW Benchmarking]]
}}

Hardware Benchmarking - Results

2026-02-14T04:53:14Z

Abrar.Alttahir: /* GPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"

! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | #
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|1
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||128||5200||32.7||108.6||141.3||JB1
|-
|2
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||64||5600||36.7||114.2||150.9||TM1
|-
|3
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs), ~3.0GHz
|3
|32
|4800
|41.9
|122.4
|164.3
|RA1
|-
|4
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||2.0||128||4000||41.2||124.1||165.3||AW1
|-
|5
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs)||3.2||128||4800||38.5||127.0||165.5||SB1
|-
|6
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz||4.5||64||4800||33.4||136.0||169.4||PSM
|-
|7
|12th Gen Intel(R) Core(TM) i9-12900K||3.2||128||3200||40.7||129.9||170.6||DD2
|-
|8
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz||2.1||64||4800||41||137.2||178.2||RB1
|-
|9
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs) @ ~4.2GHz||4.2||32||4800||35.5||162.8||198.3||LC1
|-
|10
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs)||2.5||16||3200||48.4||170.5||218.9||TS1
|-
|11
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz||3.2||64||4800||53.3||169.1||222.4||CHR
|-
|12
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|13
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|14
|AMD Ryzen 5 7600X 6-Core Processor||4.7||32||4800||39.0||175.7||214.7||HP1
|-
|15
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|16
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|17
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|18
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|19
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||3.6||64||2133||62.7||191.1||253.8||PA1
|-
|20
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|21
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|22
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|23
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|3.2
|450
|
|50.0
|211.6
|261.6
|YW1
|-
|24
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|25
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|26
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||3.2||56||N/A||49.1||218.9||268||RBR
|-
|27
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|28
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|29
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|30
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|31
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|32
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|33
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|34
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||32||2113||68.2||217.9||286.1||HP2
|-
|35
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|36
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|37
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|38
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|39
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|40
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||3.5||128||2666||66.3||234.7||301||JG3
|-
|41
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|42
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|43
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|44
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|45
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|2.6
|32
|3200
|66.6
|243.3
|309.9
|SK1
|-
|46
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|47
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|48
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|49
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz||1.4||16||7467||173.8||152.8||326.6||AL1
|-
|50
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||3.7||64||2933||95.4||231.3||326.7||JB2
|-
|51
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|52
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|53
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|54
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|55
|AMD EPYC 7V12 64-Core Processor (16 CPUs) ||2.4||57||NA||63.5||277.6||341.1||CS2
|-
|56
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz (12 CPUs), ~3.7GHz
|3.7
|32
|2666
|91.45
|259.63
|351.1
|SS1
|-
|57
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|58
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|59
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|60
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|61
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||3.8||64||3200||87.8||294.2||382||HDR-A2000
|-
|62
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|63
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|64
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|65
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|66
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|67
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|68
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|69
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz ||3.0||4||986||84.0||351.4||435.4||TJS
|-
|70
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||3.5||16||1333||125.9||320.5||446.4||EFC
|-
|71
|Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz||1.9||16||1800||100.1||347.0||447.1||SM1
|-
|72
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|73
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||4.1||16||4104||62.7||386.6||449.3||GZH
|-
|74
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||2.4||128||2394||130.5||331.5||462||VDI
|-
|75
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|76
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|77
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|78
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|79
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|80
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|81
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|}

=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | #
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|1
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|2.5
|9.5
|12.0
|JW3
|-
|2
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz ||NVIDIA GeForce RTX 4090||24||16384||2.4||11.6||14.0||JW1
|-
|3
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4090||24||16384||2.5||12.1||14.6||JB1
|-
|4
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz ||NVIDIA GeForce RTX 4090||24||16384||2.7||12.7||15.4||TA1
|-
|5
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||2.9||12.7||15.6||PSM
|-
|6
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||3.1||13.3||16.4||CR4
|-
|7
|Intel(R) Core(TM) i9-14900K (32 CPUs) @ ~3.2GHz||NVIDIA GeForce RTX 4080 Super||16||10240||2.8||14.5||17.3||DF2
|-
|8
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||TM1
|-
|9
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||JM3
|-
|10
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||NVIDIA GeForce RTX 4080||16||9728||3.2||15.0||18.2||AW1
|-
|11
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|3.2
|15.0
|18.2
|TM2
|-
|12
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs)@ ~4.2GHz||NVIDIA GeForce RTX 4080 SUPER||16||10240||3.2||15.7||18.9||LC1
|-
|13
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||NVIDIA RTX 6000 Ada Generation||48||18176||4.9||14.2||19.1||JG3
|-
|14
|Intel(R) Xeon(R) CPU E5-2697 v4 @ 2.30GHz (72 CPUs)||NVIDIA GeForce RTX 4090||24||16384||3.9||16.2||20.1||CS3
|-
|15
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|16
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||3.4||19.2||22.6||HP1
|-
|17
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|3.9
|19.07
|22.97
|PW1
|-
|18
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||3.8||19.2||23.0||RS1
|-
|19
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||3.7||19.4||23.1||DD2
|-
|20
|12th Gen Intel(R) Core(TM) i7-12700F
|NVIDIA GeForce RTX 3080 Ti
|12
|10240
|3.75
|19.57
|23.32
|AUBNE1PC6602
|-
|21
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||4.0||19.6||23.6||GP1
|-
|22
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA A40||48||10752||3.5||20.3||23.8||Lenovo SR650 V2
|-
|23
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.1||20.1||24.2||JMM
|-
|24
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|25
|Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz (36 CPUs), ~3.0GHz
|NVIDIA RTX A6000
|48
|10752
|4.7
|22.7
|27.4
|CB2
|-
|26
|13th Gen Intel(R) Core(TM) i5-13600HX (20 CPUs) @ ~2.6GHz ||NVIDIA GeForce RTX 4090 Laptop GPU||16||9728||5.3||22.2||27.5||LC2
|-
|27
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz ||NVIDIA GeForce RTX 3080||10||8704||4.3||23.7||28||RB1
|-
|28
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|29
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs) ||NVIDIA RTX A5000||24||8192||4.4||24.1||28.5||SB1
|-
|30
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|31
|Intel(R) Xeon(R) W-2223 CPU @ 3.60GHz ||NVIDIA GeForce RTX 3090||24||10496||5.3||24.3||29.6||SSM2
|-
|32
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|33
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|34
|AMD Ryzen Threadripper 2920X 12-Core Processor (24 CPUs) @ 3.5GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.1||25.9||31||CR5
|-
|35
|Intel(R) Xeon(R) W-2145 CPU @ 3.70GHz (16 CPUs), ~3.7GHz
|NVIDIA RTX A5000
|24
|8192
|5.2
|26.3
|31.5
|CB3
|-
|36
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|37
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|38
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|39
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|40
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|41
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|42
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.9||28.4||34.3||HP2
|-
|43
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|44
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|45
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|46
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|47
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|48
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|49
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|50
|Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz ||NVIDIA GRID RTX8000P-48Q||48||4608||6.8||31.0||37.8||VJ1
|-
|51
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs) ||NVIDIA RTX A4000||16||6144||5.8||32.0||37.8||JS2
|-
|52
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|53
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||6.0||32.0||38.0||EFC
|-
|54
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|7.2
|31.4
|38.6
|YW1
|-
|55
|AMD EPYC 74F3 24-Core Processor (36 CPUs) @ 3.2GHz ||NVIDIA A10-24Q||24||8192||7.2||31.4||38.6||CS1
|-
|56
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||33.3||38.7||PA1
|-
|57
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce RTX 3060 Ti (LHR) ||8||4864||5.7||33.3||39.0||DF1
|-
|58
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||NVIDIA GeForce RTX 2060 SUPER (core 1647MHz, mem 1750MHz)||8||2176||5.6||33.4||39.1||GZH
|-
|59
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|60
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|61
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||35.1||41.6||JB2
|-
|62
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|63
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|64
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz ||NVIDIA RTX A4000||16||6144||7.2||35.3||42.5||CHR
|-
|65
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|66
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|67
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|68
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|69
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|70
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|71
|AMD Ryzen 7 7840HS ||NVIDIA GeForce RTX 4060 Laptop GPU||8||3072||7.0||40.6||47.6||CH1
|-
|72
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|73
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||NVIDIA GRID RTX6000P-4Q||24||4608||10.5||39.3||49.8||VDI
|-
|74
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|75
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA Tesla T4||16||2560||6.8||45.2||52.0||RJ1
|-
|76
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz
|NVIDIA GeForce RTX 3060
|8
|3584
|7.68
|45.92
|53.6
|AUBNEW1DQ54G3
|-
|77
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|78
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|79
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|80
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|81
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz (12 CPUs), ~3.7GHz
|NVIDIA GeForce GTX 1080
|8
|2560
|9.13
|52.13
|61.3
|SS1
|-
|82
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|83
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||NVIDIA RTX A2000||6||3328||9.1||54.4||63.5||HDR-A2000
|-
|84
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|85
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz ||NVIDIA GeForce RTX 4050 Laptop GPU||6||2560||7.9||59.4||67.3||AL1
|-
|86
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs), ~3.0GHz
|NVIDIA GeForce RTX A2000
|12
|3328
|8.6
|59.5
|68.1
|RA1
|-
|87
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|88
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA GeForce GTX 1660||6||1408||9.4||61.0||70.4||JFP
|-
|89
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|90
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce GTX 1650 SUPER||4||1280||10.3||64.6||74.9||TS1
|-
|91
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|92
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|93
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|NVIDIA RTX A2000 Laptop GPU
|4
|2560
|11.4
|72.6
|84.0
|SK1
|-
|94
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|95
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|96
|Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz (64 CPUs), ~2.3GHz||NVIDIA Tesla M60||16||4096||12||82.7||94.7||SM2
|-
|97
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|98
|Intel(R) Core(TM) i3-8100 CPU @ 3.60GHz ||NVIDIA Tesla K40c||12||2880||12.3||83.1||95.3||NWE
|-
|99
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA Quadro T2000||4||1024||13.3||85.2||95.5||SSM
|-
|100
|Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz (Laptop) ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|101
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs), ~3.8GHz
|NVIDIA Quadro T1000
|4
|896
|13.6
|91.3
|104.9
|CB4
|-
|102
|13th Gen Intel(R) Core(TM) i7-13800H (20 CPUs), ~2.5GHz
|NVIDIA RTX A500 Laptop GPU
|4
|2048
|14.8
|93.9
|108.7
|YB1
|-
|103
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|104
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|105
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|106
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|107
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|108
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|109
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|110
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|111
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|112
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|113
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz||NVIDIA T500||4||986||29.3||197.5||226.8||TJS
|-
|114
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|115
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|116
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||NVIDIA A10-4Q||24||8192||44.5||212.6||257.1||RBR
|-
|117
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|118
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|119
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|120
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|}

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-

|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|73.3
|568.7
|642.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||NVIDIA GeForce RTX 4090||24||16384||103.3||811.2||914.5||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz||NVIDIA GeForce RTX 4090||24||16384||105.9||813.7||919.6||TA1
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz||NVIDIA GeForce RTX 4090||24||16384||103.7||824.3||928.0||JW1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||108.2||821.5||929.7||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||106.8||875.5||982.3||CR4
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz ||NVIDIA RTX 6000 Ada Generation||48||18176||119.6||877.8||997.4||JG3
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|134.0
|1049.9
|1183.9
|TM2
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||139.5||1115.1||1254.6||JM3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||155.5||1176.2||1331.7||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||159.7||1216.2||1375.9||GP1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||160.9||1240||1400.9||DD2
|-
|Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz ||NVIDIA GeForce RTX 3090||24||10496||172.9||1249.8||1422.7||SIP
|-
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|172.5
|1343.4
|15.15.9
|PW1
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||190.2||1493.2||1683.4||HP1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||215.9||1678.7||1894.6||PA2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||255.4||1691.7||1947.1||HP2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|242.3
|1856.5
|2098.8
|YW1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking | Back to TUFLOW Benchmarking]]
}}

Hardware Benchmarking - Results

2026-02-14T04:42:05Z

Abrar.Alttahir: /* CPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"

! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | #
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|1
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||128||5200||32.7||108.6||141.3||JB1
|-
|2
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||64||5600||36.7||114.2||150.9||TM1
|-
|3
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs), ~3.0GHz
|3
|32
|4800
|41.9
|122.4
|164.3
|RA1
|-
|4
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||2.0||128||4000||41.2||124.1||165.3||AW1
|-
|5
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs)||3.2||128||4800||38.5||127.0||165.5||SB1
|-
|6
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz||4.5||64||4800||33.4||136.0||169.4||PSM
|-
|7
|12th Gen Intel(R) Core(TM) i9-12900K||3.2||128||3200||40.7||129.9||170.6||DD2
|-
|8
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz||2.1||64||4800||41||137.2||178.2||RB1
|-
|9
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs) @ ~4.2GHz||4.2||32||4800||35.5||162.8||198.3||LC1
|-
|10
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs)||2.5||16||3200||48.4||170.5||218.9||TS1
|-
|11
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz||3.2||64||4800||53.3||169.1||222.4||CHR
|-
|12
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|13
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|14
|AMD Ryzen 5 7600X 6-Core Processor||4.7||32||4800||39.0||175.7||214.7||HP1
|-
|15
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|16
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|17
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|18
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|19
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||3.6||64||2133||62.7||191.1||253.8||PA1
|-
|20
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|21
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|22
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|23
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|3.2
|450
|
|50.0
|211.6
|261.6
|YW1
|-
|24
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|25
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|26
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||3.2||56||N/A||49.1||218.9||268||RBR
|-
|27
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|28
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|29
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|30
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|31
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|32
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|33
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|34
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||32||2113||68.2||217.9||286.1||HP2
|-
|35
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|36
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|37
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|38
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|39
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|40
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||3.5||128||2666||66.3||234.7||301||JG3
|-
|41
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|42
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|43
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|44
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|45
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|2.6
|32
|3200
|66.6
|243.3
|309.9
|SK1
|-
|46
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|47
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|48
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|49
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz||1.4||16||7467||173.8||152.8||326.6||AL1
|-
|50
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||3.7||64||2933||95.4||231.3||326.7||JB2
|-
|51
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|52
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|53
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|54
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|55
|AMD EPYC 7V12 64-Core Processor (16 CPUs) ||2.4||57||NA||63.5||277.6||341.1||CS2
|-
|56
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz (12 CPUs), ~3.7GHz
|3.7
|32
|2666
|91.45
|259.63
|351.1
|SS1
|-
|57
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|58
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|59
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|60
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|61
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||3.8||64||3200||87.8||294.2||382||HDR-A2000
|-
|62
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|63
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|64
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|65
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|66
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|67
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|68
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|69
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz ||3.0||4||986||84.0||351.4||435.4||TJS
|-
|70
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||3.5||16||1333||125.9||320.5||446.4||EFC
|-
|71
|Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz||1.9||16||1800||100.1||347.0||447.1||SM1
|-
|72
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|73
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||4.1||16||4104||62.7||386.6||449.3||GZH
|-
|74
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||2.4||128||2394||130.5||331.5||462||VDI
|-
|75
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|76
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|77
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|78
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|79
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|80
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|81
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|}

=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|2.5
|9.5
|12.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz ||NVIDIA GeForce RTX 4090||24||16384||2.4||11.6||14.0||JW1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4090||24||16384||2.5||12.1||14.6||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz ||NVIDIA GeForce RTX 4090||24||16384||2.7||12.7||15.4||TA1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||2.9||12.7||15.6||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||3.1||13.3||16.4||CR4
|-
|Intel(R) Core(TM) i9-14900K (32 CPUs) @ ~3.2GHz||NVIDIA GeForce RTX 4080 Super||16||10240||2.8||14.5||17.3||DF2
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||TM1
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||JM3
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||NVIDIA GeForce RTX 4080||16||9728||3.2||15.0||18.2||AW1
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|3.2
|15.0
|18.2
|TM2
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs)@ ~4.2GHz||NVIDIA GeForce RTX 4080 SUPER||16||10240||3.2||15.7||18.9||LC1
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||NVIDIA RTX 6000 Ada Generation||48||18176||4.9||14.2||19.1||JG3
|-
|Intel(R) Xeon(R) CPU E5-2697 v4 @ 2.30GHz (72 CPUs)||NVIDIA GeForce RTX 4090||24||16384||3.9||16.2||20.1||CS3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||3.4||19.2||22.6||HP1
|-
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|3.9
|19.07
|22.97
|PW1
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||3.8||19.2||23.0||RS1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||3.7||19.4||23.1||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700F
|NVIDIA GeForce RTX 3080 Ti
|12
|10240
|3.75
|19.57
|23.32
|AUBNE1PC6602
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||4.0||19.6||23.6||GP1
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA A40||48||10752||3.5||20.3||23.8||Lenovo SR650 V2
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.1||20.1||24.2||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz (36 CPUs), ~3.0GHz
|NVIDIA RTX A6000
|48
|10752
|4.7
|22.7
|27.4
|CB2
|-
|13th Gen Intel(R) Core(TM) i5-13600HX (20 CPUs) @ ~2.6GHz ||NVIDIA GeForce RTX 4090 Laptop GPU||16||9728||5.3||22.2||27.5||LC2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz ||NVIDIA GeForce RTX 3080||10||8704||4.3||23.7||28||RB1
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs) ||NVIDIA RTX A5000||24||8192||4.4||24.1||28.5||SB1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Xeon(R) W-2223 CPU @ 3.60GHz ||NVIDIA GeForce RTX 3090||24||10496||5.3||24.3||29.6||SSM2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen Threadripper 2920X 12-Core Processor (24 CPUs) @ 3.5GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.1||25.9||31||CR5
|-
|Intel(R) Xeon(R) W-2145 CPU @ 3.70GHz (16 CPUs), ~3.7GHz
|NVIDIA RTX A5000
|24
|8192
|5.2
|26.3
|31.5
|CB3
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.9||28.4||34.3||HP2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz ||NVIDIA GRID RTX8000P-48Q||48||4608||6.8||31.0||37.8||VJ1
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs) ||NVIDIA RTX A4000||16||6144||5.8||32.0||37.8||JS2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||6.0||32.0||38.0||EFC
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|7.2
|31.4
|38.6
|YW1
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs) @ 3.2GHz ||NVIDIA A10-24Q||24||8192||7.2||31.4||38.6||CS1
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||33.3||38.7||PA1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce RTX 3060 Ti (LHR) ||8||4864||5.7||33.3||39.0||DF1
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||NVIDIA GeForce RTX 2060 SUPER (core 1647MHz, mem 1750MHz)||8||2176||5.6||33.4||39.1||GZH
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||35.1||41.6||JB2
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz ||NVIDIA RTX A4000||16||6144||7.2||35.3||42.5||CHR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|AMD Ryzen 7 7840HS ||NVIDIA GeForce RTX 4060 Laptop GPU||8||3072||7.0||40.6||47.6||CH1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||NVIDIA GRID RTX6000P-4Q||24||4608||10.5||39.3||49.8||VDI
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA Tesla T4||16||2560||6.8||45.2||52.0||RJ1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz
|NVIDIA GeForce RTX 3060
|8
|3584
|7.68
|45.92
|53.6
|AUBNEW1DQ54G3
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz (12 CPUs), ~3.7GHz
|NVIDIA GeForce GTX 1080
|8
|2560
|9.13
|52.13
|61.3
|SS1
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||NVIDIA RTX A2000||6||3328||9.1||54.4||63.5||HDR-A2000
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz ||NVIDIA GeForce RTX 4050 Laptop GPU||6||2560||7.9||59.4||67.3||AL1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs), ~3.0GHz
|NVIDIA GeForce RTX A2000
|12
|3328
|8.6
|59.5
|68.1
|RA1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA GeForce GTX 1660||6||1408||9.4||61.0||70.4||JFP
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce GTX 1650 SUPER||4||1280||10.3||64.6||74.9||TS1
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|NVIDIA RTX A2000 Laptop GPU
|4
|2560
|11.4
|72.6
|84.0
|SK1
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz (64 CPUs), ~2.3GHz||NVIDIA Tesla M60||16||4096||12||82.7||94.7||SM2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i3-8100 CPU @ 3.60GHz ||NVIDIA Tesla K40c||12||2880||12.3||83.1||95.3||NWE
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA Quadro T2000||4||1024||13.3||85.2||95.5||SSM
|-
|Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz (Laptop) ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs), ~3.8GHz
|NVIDIA Quadro T1000
|4
|896
|13.6
|91.3
|104.9
|CB4
|-
|13th Gen Intel(R) Core(TM) i7-13800H (20 CPUs), ~2.5GHz
|NVIDIA RTX A500 Laptop GPU
|4
|2048
|14.8
|93.9
|108.7
|YB1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz||NVIDIA T500||4||986||29.3||197.5||226.8||TJS
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||NVIDIA A10-4Q||24||8192||44.5||212.6||257.1||RBR
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-

|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|73.3
|568.7
|642.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||NVIDIA GeForce RTX 4090||24||16384||103.3||811.2||914.5||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz||NVIDIA GeForce RTX 4090||24||16384||105.9||813.7||919.6||TA1
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz||NVIDIA GeForce RTX 4090||24||16384||103.7||824.3||928.0||JW1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||108.2||821.5||929.7||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||106.8||875.5||982.3||CR4
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz ||NVIDIA RTX 6000 Ada Generation||48||18176||119.6||877.8||997.4||JG3
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|134.0
|1049.9
|1183.9
|TM2
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||139.5||1115.1||1254.6||JM3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||155.5||1176.2||1331.7||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||159.7||1216.2||1375.9||GP1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||160.9||1240||1400.9||DD2
|-
|Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz ||NVIDIA GeForce RTX 3090||24||10496||172.9||1249.8||1422.7||SIP
|-
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|172.5
|1343.4
|15.15.9
|PW1
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||190.2||1493.2||1683.4||HP1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||215.9||1678.7||1894.6||PA2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||255.4||1691.7||1947.1||HP2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|242.3
|1856.5
|2098.8
|YW1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking | Back to TUFLOW Benchmarking]]
}}

Hardware Benchmarking - Results

2026-02-13T08:02:32Z

Abrar.Alttahir: /* GPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"

! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||128||5200||32.7||108.6||141.3||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||64||5600||36.7||114.2||150.9||TM1
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||2.0||128||4000||41.2||124.1||165.3||AW1
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs)||3.2||128||4800||38.5||127.0||165.5||SB1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz||4.5||64||4800||33.4||136.0||169.4||PSM
|-
|12th Gen Intel(R) Core(TM) i9-12900K||3.2||128||3200||40.7||129.9||170.6||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz||2.1||64||4800||41||137.2||178.2||RB1
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs) @ ~4.2GHz||4.2||32||4800||35.5||162.8||198.3||LC1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs)||2.5||16||3200||48.4||170.5||218.9||TS1
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz||3.2||64||4800||53.3||169.1||222.4||CHR
|-
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|AMD Ryzen 5 7600X 6-Core Processor||4.7||32||4800||39.0||175.7||214.7||HP1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||3.6||64||2133||62.7||191.1||253.8||PA1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|3.2
|450
|
|50.0
|211.6
|261.6
|YW1
|-
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||3.2||56||N/A||49.1||218.9||268||RBR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||32||2113||68.2||217.9||286.1||HP2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||3.5||128||2666||66.3||234.7||301||JG3
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|2.6
|32
|3200
|66.6
|243.3
|309.9
|SK1
|-
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz||1.4||16||7467||173.8||152.8||326.6||AL1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||3.7||64||2933||95.4||231.3||326.7||JB2
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|AMD EPYC 7V12 64-Core Processor (16 CPUs) ||2.4||57||NA||63.5||277.6||341.1||CS2
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz (12 CPUs), ~3.7GHz
|3.7
|32
|2666
|91.45
|259.63
|351.1
|SS1
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||3.8||64||3200||87.8||294.2||382||HDR-A2000
|-
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz ||3.0||4||986||84.0||351.4||435.4||TJS
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||3.5||16||1333||125.9||320.5||446.4||EFC
|-
|Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz||1.9||16||1800||100.1||347.0||447.1||SM1
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||4.1||16||4104||62.7||386.6||449.3||GZH
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||2.4||128||2394||130.5||331.5||462||VDI
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|-
|}

=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|2.5
|9.5
|12.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz ||NVIDIA GeForce RTX 4090||24||16384||2.4||11.6||14.0||JW1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4090||24||16384||2.5||12.1||14.6||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz ||NVIDIA GeForce RTX 4090||24||16384||2.7||12.7||15.4||TA1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||2.9||12.7||15.6||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||3.1||13.3||16.4||CR4
|-
|Intel(R) Core(TM) i9-14900K (32 CPUs) @ ~3.2GHz||NVIDIA GeForce RTX 4080 Super||16||10240||2.8||14.5||17.3||DF2
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||TM1
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||JM3
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||NVIDIA GeForce RTX 4080||16||9728||3.2||15.0||18.2||AW1
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|3.2
|15.0
|18.2
|TM2
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs)@ ~4.2GHz||NVIDIA GeForce RTX 4080 SUPER||16||10240||3.2||15.7||18.9||LC1
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||NVIDIA RTX 6000 Ada Generation||48||18176||4.9||14.2||19.1||JG3
|-
|Intel(R) Xeon(R) CPU E5-2697 v4 @ 2.30GHz (72 CPUs)||NVIDIA GeForce RTX 4090||24||16384||3.9||16.2||20.1||CS3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||3.4||19.2||22.6||HP1
|-
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|3.9
|19.07
|22.97
|PW1
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||3.8||19.2||23.0||RS1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||3.7||19.4||23.1||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700F
|NVIDIA GeForce RTX 3080 Ti
|12
|10240
|3.75
|19.57
|23.32
|AUBNE1PC6602
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||4.0||19.6||23.6||GP1
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA A40||48||10752||3.5||20.3||23.8||Lenovo SR650 V2
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.1||20.1||24.2||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz (36 CPUs), ~3.0GHz
|NVIDIA RTX A6000
|48
|10752
|4.7
|22.7
|27.4
|CB2
|-
|13th Gen Intel(R) Core(TM) i5-13600HX (20 CPUs) @ ~2.6GHz ||NVIDIA GeForce RTX 4090 Laptop GPU||16||9728||5.3||22.2||27.5||LC2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz ||NVIDIA GeForce RTX 3080||10||8704||4.3||23.7||28||RB1
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs) ||NVIDIA RTX A5000||24||8192||4.4||24.1||28.5||SB1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Xeon(R) W-2223 CPU @ 3.60GHz ||NVIDIA GeForce RTX 3090||24||10496||5.3||24.3||29.6||SSM2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen Threadripper 2920X 12-Core Processor (24 CPUs) @ 3.5GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.1||25.9||31||CR5
|-
|Intel(R) Xeon(R) W-2145 CPU @ 3.70GHz (16 CPUs), ~3.7GHz
|NVIDIA RTX A5000
|24
|8192
|5.2
|26.3
|31.5
|CB3
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.9||28.4||34.3||HP2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz ||NVIDIA GRID RTX8000P-48Q||48||4608||6.8||31.0||37.8||VJ1
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs) ||NVIDIA RTX A4000||16||6144||5.8||32.0||37.8||JS2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||6.0||32.0||38.0||EFC
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|7.2
|31.4
|38.6
|YW1
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs) @ 3.2GHz ||NVIDIA A10-24Q||24||8192||7.2||31.4||38.6||CS1
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||33.3||38.7||PA1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce RTX 3060 Ti (LHR) ||8||4864||5.7||33.3||39.0||DF1
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||NVIDIA GeForce RTX 2060 SUPER (core 1647MHz, mem 1750MHz)||8||2176||5.6||33.4||39.1||GZH
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||35.1||41.6||JB2
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz ||NVIDIA RTX A4000||16||6144||7.2||35.3||42.5||CHR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|AMD Ryzen 7 7840HS ||NVIDIA GeForce RTX 4060 Laptop GPU||8||3072||7.0||40.6||47.6||CH1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||NVIDIA GRID RTX6000P-4Q||24||4608||10.5||39.3||49.8||VDI
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA Tesla T4||16||2560||6.8||45.2||52.0||RJ1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz
|NVIDIA GeForce RTX 3060
|8
|3584
|7.68
|45.92
|53.6
|AUBNEW1DQ54G3
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz (12 CPUs), ~3.7GHz
|NVIDIA GeForce GTX 1080
|8
|2560
|9.13
|52.13
|61.3
|SS1
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||NVIDIA RTX A2000||6||3328||9.1||54.4||63.5||HDR-A2000
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz ||NVIDIA GeForce RTX 4050 Laptop GPU||6||2560||7.9||59.4||67.3||AL1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA GeForce GTX 1660||6||1408||9.4||61.0||70.4||JFP
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce GTX 1650 SUPER||4||1280||10.3||64.6||74.9||TS1
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|NVIDIA RTX A2000 Laptop GPU
|4
|2560
|11.4
|72.6
|84.0
|SK1
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz (64 CPUs), ~2.3GHz||NVIDIA Tesla M60||16||4096||12||82.7||94.7||SM2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i3-8100 CPU @ 3.60GHz ||NVIDIA Tesla K40c||12||2880||12.3||83.1||95.3||NWE
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA Quadro T2000||4||1024||13.3||85.2||95.5||SSM
|-
|Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz (Laptop) ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs), ~3.8GHz
|NVIDIA Quadro T1000
|4
|896
|13.6
|91.3
|104.9
|CB4
|-
|13th Gen Intel(R) Core(TM) i7-13800H (20 CPUs), ~2.5GHz
|NVIDIA RTX A500 Laptop GPU
|4
|2048
|14.8
|93.9
|108.7
|YB1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz||NVIDIA T500||4||986||29.3||197.5||226.8||TJS
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||NVIDIA A10-4Q||24||8192||44.5||212.6||257.1||RBR
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-

|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|73.3
|568.7
|642.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||NVIDIA GeForce RTX 4090||24||16384||103.3||811.2||914.5||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz||NVIDIA GeForce RTX 4090||24||16384||105.9||813.7||919.6||TA1
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz||NVIDIA GeForce RTX 4090||24||16384||103.7||824.3||928.0||JW1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||108.2||821.5||929.7||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||106.8||875.5||982.3||CR4
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz ||NVIDIA RTX 6000 Ada Generation||48||18176||119.6||877.8||997.4||JG3
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|134.0
|1049.9
|1183.9
|TM2
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||139.5||1115.1||1254.6||JM3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||155.5||1176.2||1331.7||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||159.7||1216.2||1375.9||GP1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||160.9||1240||1400.9||DD2
|-
|Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz ||NVIDIA GeForce RTX 3090||24||10496||172.9||1249.8||1422.7||SIP
|-
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|172.5
|1343.4
|15.15.9
|PW1
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||190.2||1493.2||1683.4||HP1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||215.9||1678.7||1894.6||PA2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||255.4||1691.7||1947.1||HP2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|242.3
|1856.5
|2098.8
|YW1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking | Back to TUFLOW Benchmarking]]
}}

Hardware Benchmarking - Results

2026-02-13T07:56:43Z

Abrar.Alttahir: /* CPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"

! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||128||5200||32.7||108.6||141.3||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||64||5600||36.7||114.2||150.9||TM1
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||2.0||128||4000||41.2||124.1||165.3||AW1
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs)||3.2||128||4800||38.5||127.0||165.5||SB1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz||4.5||64||4800||33.4||136.0||169.4||PSM
|-
|12th Gen Intel(R) Core(TM) i9-12900K||3.2||128||3200||40.7||129.9||170.6||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz||2.1||64||4800||41||137.2||178.2||RB1
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs) @ ~4.2GHz||4.2||32||4800||35.5||162.8||198.3||LC1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs)||2.5||16||3200||48.4||170.5||218.9||TS1
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz||3.2||64||4800||53.3||169.1||222.4||CHR
|-
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|AMD Ryzen 5 7600X 6-Core Processor||4.7||32||4800||39.0||175.7||214.7||HP1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||3.6||64||2133||62.7||191.1||253.8||PA1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|3.2
|450
|
|50.0
|211.6
|261.6
|YW1
|-
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||3.2||56||N/A||49.1||218.9||268||RBR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||32||2113||68.2||217.9||286.1||HP2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||3.5||128||2666||66.3||234.7||301||JG3
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|2.6
|32
|3200
|66.6
|243.3
|309.9
|SK1
|-
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz||1.4||16||7467||173.8||152.8||326.6||AL1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||3.7||64||2933||95.4||231.3||326.7||JB2
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|AMD EPYC 7V12 64-Core Processor (16 CPUs) ||2.4||57||NA||63.5||277.6||341.1||CS2
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz (12 CPUs), ~3.7GHz
|3.7
|32
|2666
|91.45
|259.63
|351.1
|SS1
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||3.8||64||3200||87.8||294.2||382||HDR-A2000
|-
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz ||3.0||4||986||84.0||351.4||435.4||TJS
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||3.5||16||1333||125.9||320.5||446.4||EFC
|-
|Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz||1.9||16||1800||100.1||347.0||447.1||SM1
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||4.1||16||4104||62.7||386.6||449.3||GZH
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||2.4||128||2394||130.5||331.5||462||VDI
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|-
|}

=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|2.5
|9.5
|12.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz ||NVIDIA GeForce RTX 4090||24||16384||2.4||11.6||14.0||JW1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4090||24||16384||2.5||12.1||14.6||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz ||NVIDIA GeForce RTX 4090||24||16384||2.7||12.7||15.4||TA1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||2.9||12.7||15.6||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||3.1||13.3||16.4||CR4
|-
|Intel(R) Core(TM) i9-14900K (32 CPUs) @ ~3.2GHz||NVIDIA GeForce RTX 4080 Super||16||10240||2.8||14.5||17.3||DF2
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||TM1
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||JM3
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||NVIDIA GeForce RTX 4080||16||9728||3.2||15.0||18.2||AW1
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|3.2
|15.0
|18.2
|TM2
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs)@ ~4.2GHz||NVIDIA GeForce RTX 4080 SUPER||16||10240||3.2||15.7||18.9||LC1
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||NVIDIA RTX 6000 Ada Generation||48||18176||4.9||14.2||19.1||JG3
|-
|Intel(R) Xeon(R) CPU E5-2697 v4 @ 2.30GHz (72 CPUs)||NVIDIA GeForce RTX 4090||24||16384||3.9||16.2||20.1||CS3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||3.4||19.2||22.6||HP1
|-
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|3.9
|19.07
|22.97
|PW1
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||3.8||19.2||23.0||RS1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||3.7||19.4||23.1||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700F
|NVIDIA GeForce RTX 3080 Ti
|12
|10240
|3.75
|19.57
|23.32
|AUBNE1PC6602
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||4.0||19.6||23.6||GP1
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA A40||48||10752||3.5||20.3||23.8||Lenovo SR650 V2
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.1||20.1||24.2||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz (36 CPUs), ~3.0GHz
|NVIDIA RTX A6000
|48
|10752
|4.7
|22.7
|27.4
|CB2
|-
|13th Gen Intel(R) Core(TM) i5-13600HX (20 CPUs) @ ~2.6GHz ||NVIDIA GeForce RTX 4090 Laptop GPU||16||9728||5.3||22.2||27.5||LC2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz ||NVIDIA GeForce RTX 3080||10||8704||4.3||23.7||28||RB1
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs) ||NVIDIA RTX A5000||24||8192||4.4||24.1||28.5||SB1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Xeon(R) W-2223 CPU @ 3.60GHz ||NVIDIA GeForce RTX 3090||24||10496||5.3||24.3||29.6||SSM2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen Threadripper 2920X 12-Core Processor (24 CPUs) @ 3.5GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.1||25.9||31||CR5
|-
|Intel(R) Xeon(R) W-2145 CPU @ 3.70GHz (16 CPUs), ~3.7GHz
|NVIDIA RTX A5000
|24
|8192
|5.2
|26.3
|31.5
|CB3
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.9||28.4||34.3||HP2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz ||NVIDIA GRID RTX8000P-48Q||48||4608||6.8||31.0||37.8||VJ1
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs) ||NVIDIA RTX A4000||16||6144||5.8||32.0||37.8||JS2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||6.0||32.0||38.0||EFC
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|7.2
|31.4
|38.6
|YW1
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs) @ 3.2GHz ||NVIDIA A10-24Q||24||8192||7.2||31.4||38.6||CS1
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||33.3||38.7||PA1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce RTX 3060 Ti (LHR) ||8||4864||5.7||33.3||39.0||DF1
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||NVIDIA GeForce RTX 2060 SUPER (core 1647MHz, mem 1750MHz)||8||2176||5.6||33.4||39.1||GZH
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||35.1||41.6||JB2
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz ||NVIDIA RTX A4000||16||6144||7.2||35.3||42.5||CHR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|AMD Ryzen 7 7840HS ||NVIDIA GeForce RTX 4060 Laptop GPU||8||3072||7.0||40.6||47.6||CH1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||NVIDIA GRID RTX6000P-4Q||24||4608||10.5||39.3||49.8||VDI
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA Tesla T4||16||2560||6.8||45.2||52.0||RJ1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz
|NVIDIA GeForce RTX 3060
|8
|3584
|7.68
|45.92
|53.6
|AUBNEW1DQ54G3
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||NVIDIA RTX A2000||6||3328||9.1||54.4||63.5||HDR-A2000
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz ||NVIDIA GeForce RTX 4050 Laptop GPU||6||2560||7.9||59.4||67.3||AL1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA GeForce GTX 1660||6||1408||9.4||61.0||70.4||JFP
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce GTX 1650 SUPER||4||1280||10.3||64.6||74.9||TS1
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|NVIDIA RTX A2000 Laptop GPU
|4
|2560
|11.4
|72.6
|84.0
|SK1
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz (64 CPUs), ~2.3GHz||NVIDIA Tesla M60||16||4096||12||82.7||94.7||SM2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i3-8100 CPU @ 3.60GHz ||NVIDIA Tesla K40c||12||2880||12.3||83.1||95.3||NWE
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA Quadro T2000||4||1024||13.3||85.2||95.5||SSM
|-
|Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz (Laptop) ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs), ~3.8GHz
|NVIDIA Quadro T1000
|4
|896
|13.6
|91.3
|104.9
|CB4
|-
|13th Gen Intel(R) Core(TM) i7-13800H (20 CPUs), ~2.5GHz
|NVIDIA RTX A500 Laptop GPU
|4
|2048
|14.8
|93.9
|108.7
|YB1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz||NVIDIA T500||4||986||29.3||197.5||226.8||TJS
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||NVIDIA A10-4Q||24||8192||44.5||212.6||257.1||RBR
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-

|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|73.3
|568.7
|642.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||NVIDIA GeForce RTX 4090||24||16384||103.3||811.2||914.5||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz||NVIDIA GeForce RTX 4090||24||16384||105.9||813.7||919.6||TA1
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz||NVIDIA GeForce RTX 4090||24||16384||103.7||824.3||928.0||JW1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||108.2||821.5||929.7||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||106.8||875.5||982.3||CR4
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz ||NVIDIA RTX 6000 Ada Generation||48||18176||119.6||877.8||997.4||JG3
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|134.0
|1049.9
|1183.9
|TM2
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||139.5||1115.1||1254.6||JM3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||155.5||1176.2||1331.7||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||159.7||1216.2||1375.9||GP1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||160.9||1240||1400.9||DD2
|-
|Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz ||NVIDIA GeForce RTX 3090||24||10496||172.9||1249.8||1422.7||SIP
|-
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|172.5
|1343.4
|15.15.9
|PW1
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||190.2||1493.2||1683.4||HP1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||215.9||1678.7||1894.6||PA2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||255.4||1691.7||1947.1||HP2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|242.3
|1856.5
|2098.8
|YW1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking | Back to TUFLOW Benchmarking]]
}}

TUFLOW Output Discussion

2026-02-13T04:56:07Z

Abrar.Alttahir: /* Why do the high-resolution (HR) mapped results have a significantly smaller extent than the standard (non-HR) grid outputs for a sub-grid-scale (SGS) direct rainfall HPC model? */

= Common Questions Answered (FAQ)=
== Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)? ==
Slight differences are expected between the result grids (ASC or FLT) and mesh results (XMDF or DAT) due to small differences in the interpolation method.
* When outputting directly to FLT or ASC using the <tt>Map Output Format</tt> command, the output grid results are derived from both the cell centres and cell corners and the final results has those values interpolated to the centres of half cells (by default).
* When outputting XMDF (or DAT) format, the mesh results are derived from cell centres for depths / levels and cell sides for vector data. The final XMDF stores data interpolated to the cell corners and doesn’t have any memory of where the corner data were derived from and what those values were.
The differences can also be amplified when the rotation of the model is not perfectly north-south aligned (i.e., a non-zero orientation). 

== Why are grids processed with TUFLOW_to_GIS different from TUFLOW grids? ==
Slight differences are expected between the result grids (ASC or FLT) produced directly from TUFLOW and post-processed from the TUFLOW_to_GIS utility from XMDF or DAT due to differences in the interpolation approach.
Check [[TUFLOW_Output_Discussion#Why_are_grid_outputs_.28ASC.2C_FLT.29_slightly_different_from_mesh_outputs_.28XMDF.2C_DAT.29.3F | Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)?]] question above on differences between outputs direct from the TUFLOW Engine. 
When ASC/FLT results are sampled from the XMDF files with the TUFLOW_to_GIS utility, it uses less data points than when the grids are written directly from TUFLOW.

The differences can also be amplified when the rotation of the model is a non-zero value.
The extent of the grids is also different. When TUFLOW starts it removes any redundant areas (if any) outside of 2d_code layer to reduce the calculation time:
* The extent of grids written directly from TUFLOW works with the unreduced area, encapsulating the entire rotated model domain in a north-south orientated rectangle.
* The extent of XMDF (or DAT) results is based on the reduced area. When the TUFLOW_to_GIS utility is used the post-processed north-south aligned grids encapsulate the reduced model extent. The origin coordinates of such grids doesn't match the origin of the model (model domain), neither the grids written out from TUFLOW directly.

In general, a better interpolation of results is achieved when the grids are written directly from TUFLOW than when post-processed with TUFLOW_to_GIS utility. Importantly, when comparing results the recommendation is to be consistent and to use the same method for the duration of a project to ensure there are no differences as a consequence of the post-processing method. 

== Why is Z0 hazard coming directly from TUFLOW different to the one calculated from maximum velocity and depth? ==
The maximum velocity and maximum depth doesn’t necessarily occur at the same time. The Z0 hazard written directly from TUFLOW has the depth x velocity calculated every timestep and at the end outputs the maximums of all the timesteps. When using the velocity and depth grids the maximum velocity is always used with maximum depth. 
Outputting the Z0 hazard grid directly from TUFLOW is more accurate, however, if this is not an option, manual creation is an acceptable workaround. The general recommendation is to choose one method and keep it consistent for the length of the project. 
When calculating the Z0 hazard from the velocity and depth, the grids have already been interpolated by TUFLOW before the raster calculation (less data is used for the calculation), whereas the TUFLOW Z0 output is interpolated from the calculated data directly. Please also refer to the above question for more information [[TUFLOW_Output_Discussion#Why_are_grid_outputs_.28ASC.2C_FLT.29_slightly_different_from_mesh_outputs_.28XMDF.2C_DAT.29.3F | Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)?]]. 

== Why does hazard output (Z1, Z2, ...) show float values and not just integers? ==
The hazard values are calculated in cell corners and are in a form of an integer. When the values are processed into a grid, which has, by default, a resolution of half the cell size, the values are interpolated to the cell centres - four cell centre values per cell. Further interpolation occurs when the model has a non-zero orientation as grid files are north-south aligned. 

== Why are results from 2d_po features, POMM.csv and plots extracted from XMDF in GIS package not matching? ==
There are numerous reasons for the outputs to not be matching:
* Plot output:
** The 2d_po results are tracked at every <tt>Time Series Output Interval</tt> and saved into the PO.csv file. If the water level peaks in between the intervals, that maximum water level won’t be recorded in the PO.csv.
** The POMM.csv tracks maximums of PO features at every computational timestep. 
* Map output:
** The temporal XMDF (or DAT) results are tracked every <tt>Map Output Interval</tt> and interpolated to the cell corners.
** The maximum XMDF map output records the value at every computational timestep, however the final maximum map output is also being interpolated to the cell corners. 
** The extraction tools in GIS packages use interpolated data for the plot creation, e.g. less data than the TUFLOW direct plot output.
* Although the maximums from POMM.csv and the maximum map output will be very close, they won't match exactly.

Further notes for using post-processed flux line:
* No information is stored in the map output about whether the flow is upstream or downstream controlled.
* The staggered computational grid means there is always some interpolation occurring when projecting levels and velocities to the flux line. This will cause significant uncertainty in areas of sudden topographic change such as at an embankment. It is also the reason that two similar lines side by side may provide different outputs.
* Highly variable/complex flows, which are usually associated with highly variable topography, will add to the uncertainty when interpolating to the flux line.

The general recommendation is to use a lot of 2d_po features with reasonably small <tt>Time Series Output Interval</tt> to achieve better accuracy, they won't slow the simulation down. In case of flux lines, the 2d_po lines are using the actual computed fluxes across each cell face and will be an exact representation of the flow being calculated by the 2D solver. Post-processed fluxes should only be located in areas of little topographic change and even then, used as a rough guide. 

== Why are my TIF raster outputs not updated after a model rerun? ==
The GeoTIFF raster output is supported from the 2023-03 Release and onwards. It is considered an image by File Explorer and for some users, the 'Date' in file explorer defaults to show the 'Date Created' rather than the 'Date Modified'. If it appears that your results in the 'Results' folder (e.g. XMDF) are being updated however the results in the 'Results/grid' folder (e.g. TIF) aren't, this may be the reason. 
To confirm, turn on both 'Date Created' and 'Date Modified': 
[[File:TIF Date 01.png | 500px]] 
The 'Date Modified' column will show the date of the last model run: 
[[File:TIF Date 02.png | 500px]] 
 

== How do I apply high resolution (HR) output and what are the options? ==
See the [[TUFLOW_HR_Output | TUFLOW HR Output]] Wiki page for details. 
 

== Why are there differences between high resolution grid from TUFLOW and remapped outputs generated by the asc_to_asc utility? ==
Differences are expected between the direct HR output and the [[ASC_to_ASC#Remap | remap function]] in the asc_to_asc utility due to the different steps involved in the interpolation of water level results. 
The direct HR output is based on a triangulation of the water levels at the cell centres and corners. In comparison, the remap function involves two interpolation steps. The first is the interpolation from the cell centres and corners to the grid map output locations (by default, half the cell size). Then, the asc_to_asc utility interpolates the water level output to the target DEM grids with fine resolution: 
[[File:High-res-interpolation.jpg|600px]] 

The direct HR output tends to have better resolution in comparison to the remapped results. Due to the additional stages of interpolation involved with the remapped output, there is potential to lose definition in the results particularly in areas with steep slopes.

==How to create a maximum grid for datasets without direct maximum output from a TUFLOW run?==

For cumulative datasets without direct maximum output (e.g., cumulative infiltration, cumulative rainfall etc), the map output interval in the TCF needs to match the simulation end time in seconds. The end time output is when the maximum occurs for cumulative datasets, as it represents the cumulative results over the entire simulation duration.

For non-cumulative datasets without direct maximum output (e.g., Froude number, rainfall rate etc):
*Adjust the <tt>Map Output Interval</tt> in the TCF to a small value to ensure temporal variations are captured.
*Specify XMDF for <tt>Map Output Format</tt>.
*Identify the output ID number of the desired dataset within the .xmdf file:
<pre>"C:\TUFLOW\Releases\res_to_res\res_to_res_w64.exe" -xnfo results.xmdf</pre>
*Extract maximum values using the [[RES_to_RES#Introduction | RES to RES]] utility.
*Confirm the dataset's output ID number in the resulting (maxmax).xmdf file:
<pre>"C:\TUFLOW\Releases\res_to_res\res_to_res_w64.exe" -xnfo (maxmax).xmdf</pre>
*Convert the resulting dataset to a preferred grid format for GIS use using the [[TUFLOW_to_GIS#Introduction | TUFLOW to GIS]] utility.

:Note: For non-cumulative datasets, this method does not capture the true maximum, however can be close enough with a fine map output interval.

== Why are the values of the mapped water level results (h_max) lower than the terrain elevations (DEM_Z)? ==

As at TUFLOW Build 25.1.1, there is a current limitation for result outputs at shallow sheet flow cells on steeper terrain in sub-grid-scale (SGS) enabled models. Direct rainfall models are particularly prone to result in substantial extents of shallow sheet flow.

In TUFLOW HPC, the cell averaged depth is used computationally and by default in the depth outputs (see below).

Water level outputs, however, assume that water fills from the bottom of the SGS cell’s storage curve. This water level result is recorded at the cell centre, interpolated to the cell corners, and then the gridded outputs are triangulated from these points (orange line). In sheet flow situations when the volume in that cell is small, the cell-centered water level may sit below the terrain (blue dot on diagram) if the cell is steep enough. This can result in a water level results sitting below the DEM_Z, even when there is a positive depth result. Note this does not affect the simulation computationally, only how the water level results are output. 
[[File:SGS_Sheetflow_Waterlevel.jpg|600px]] 

== Why do the high-resolution (HR) mapped results have a significantly smaller extent than the standard (non-HR) grid outputs for a sub-grid-scale (SGS) direct rainfall HPC model? ==

The high resolution (HR) outputs are based on [[TUFLOW_HR_Output | intersecting]] the standard water level output results with the HR DEM_Z. Due to the [[TUFLOW_Output_Discussion#Why_are_the_values_of_the_mapped_water_level_results_.28h_max.29_lower_than_the_terrain_elevations_.28DEM_Z.29.3F | output limitation]] for sub-grid-scale (SGS) enabled models with shallow sheet flow (particularly direct rainfall models) on sufficiently steep terrain, water levels can be output lower than the DEM_Z terrain elevations. This is due to the assumption (for output purposes) that water fills the SGS cell’s storage curve from the bottom, which differs from the approach of cell-average depth used computationally in TUFLOW HPC and by default in depth outputs. When the HR water level results are processed, the full extent of the water level results are intersected with the HR DEM_Z and any water level results that are lower than the HR DEM_Z elevations due to the above effect are trimmed. The extent of the HR water level results are then used to trim the HR depth outputs.

== When should high-resolution (HR) outputs be used, and what are their limitations? ==
HR outputs are intended for visualisation and presentation because they retain sub-grid terrain detail but do not improve the underlying hydraulic calculations.

They are not suitable for assessment, benchmarking, or detailed analysis. Standard non-HR map outputs should be used for these purposes, and accuracy should be improved by refining the 2D cell size.
{{Tips Navigation
|uplink=[[TUFLOW_Modelling_Guidance | Back to TUFLOW Modelling Guidance]]
}}

TUFLOW Output Discussion

2026-02-13T04:32:08Z

Abrar.Alttahir: /* Why the high resolution (HR) mapped results have a significantly smaller extent than the standard (non-HR) grid outputs for a sub-grid-scale (SGS) enabled direct rainfall HPC model? */

= Common Questions Answered (FAQ)=
== Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)? ==
Slight differences are expected between the result grids (ASC or FLT) and mesh results (XMDF or DAT) due to small differences in the interpolation method.
* When outputting directly to FLT or ASC using the <tt>Map Output Format</tt> command, the output grid results are derived from both the cell centres and cell corners and the final results has those values interpolated to the centres of half cells (by default).
* When outputting XMDF (or DAT) format, the mesh results are derived from cell centres for depths / levels and cell sides for vector data. The final XMDF stores data interpolated to the cell corners and doesn’t have any memory of where the corner data were derived from and what those values were.
The differences can also be amplified when the rotation of the model is not perfectly north-south aligned (i.e., a non-zero orientation). 

== Why are grids processed with TUFLOW_to_GIS different from TUFLOW grids? ==
Slight differences are expected between the result grids (ASC or FLT) produced directly from TUFLOW and post-processed from the TUFLOW_to_GIS utility from XMDF or DAT due to differences in the interpolation approach.
Check [[TUFLOW_Output_Discussion#Why_are_grid_outputs_.28ASC.2C_FLT.29_slightly_different_from_mesh_outputs_.28XMDF.2C_DAT.29.3F | Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)?]] question above on differences between outputs direct from the TUFLOW Engine. 
When ASC/FLT results are sampled from the XMDF files with the TUFLOW_to_GIS utility, it uses less data points than when the grids are written directly from TUFLOW.

The differences can also be amplified when the rotation of the model is a non-zero value.
The extent of the grids is also different. When TUFLOW starts it removes any redundant areas (if any) outside of 2d_code layer to reduce the calculation time:
* The extent of grids written directly from TUFLOW works with the unreduced area, encapsulating the entire rotated model domain in a north-south orientated rectangle.
* The extent of XMDF (or DAT) results is based on the reduced area. When the TUFLOW_to_GIS utility is used the post-processed north-south aligned grids encapsulate the reduced model extent. The origin coordinates of such grids doesn't match the origin of the model (model domain), neither the grids written out from TUFLOW directly.

In general, a better interpolation of results is achieved when the grids are written directly from TUFLOW than when post-processed with TUFLOW_to_GIS utility. Importantly, when comparing results the recommendation is to be consistent and to use the same method for the duration of a project to ensure there are no differences as a consequence of the post-processing method. 

== Why is Z0 hazard coming directly from TUFLOW different to the one calculated from maximum velocity and depth? ==
The maximum velocity and maximum depth doesn’t necessarily occur at the same time. The Z0 hazard written directly from TUFLOW has the depth x velocity calculated every timestep and at the end outputs the maximums of all the timesteps. When using the velocity and depth grids the maximum velocity is always used with maximum depth. 
Outputting the Z0 hazard grid directly from TUFLOW is more accurate, however, if this is not an option, manual creation is an acceptable workaround. The general recommendation is to choose one method and keep it consistent for the length of the project. 
When calculating the Z0 hazard from the velocity and depth, the grids have already been interpolated by TUFLOW before the raster calculation (less data is used for the calculation), whereas the TUFLOW Z0 output is interpolated from the calculated data directly. Please also refer to the above question for more information [[TUFLOW_Output_Discussion#Why_are_grid_outputs_.28ASC.2C_FLT.29_slightly_different_from_mesh_outputs_.28XMDF.2C_DAT.29.3F | Why are grid outputs (ASC, FLT) slightly different from mesh outputs (XMDF, DAT)?]]. 

== Why does hazard output (Z1, Z2, ...) show float values and not just integers? ==
The hazard values are calculated in cell corners and are in a form of an integer. When the values are processed into a grid, which has, by default, a resolution of half the cell size, the values are interpolated to the cell centres - four cell centre values per cell. Further interpolation occurs when the model has a non-zero orientation as grid files are north-south aligned. 

== Why are results from 2d_po features, POMM.csv and plots extracted from XMDF in GIS package not matching? ==
There are numerous reasons for the outputs to not be matching:
* Plot output:
** The 2d_po results are tracked at every <tt>Time Series Output Interval</tt> and saved into the PO.csv file. If the water level peaks in between the intervals, that maximum water level won’t be recorded in the PO.csv.
** The POMM.csv tracks maximums of PO features at every computational timestep. 
* Map output:
** The temporal XMDF (or DAT) results are tracked every <tt>Map Output Interval</tt> and interpolated to the cell corners.
** The maximum XMDF map output records the value at every computational timestep, however the final maximum map output is also being interpolated to the cell corners. 
** The extraction tools in GIS packages use interpolated data for the plot creation, e.g. less data than the TUFLOW direct plot output.
* Although the maximums from POMM.csv and the maximum map output will be very close, they won't match exactly.

Further notes for using post-processed flux line:
* No information is stored in the map output about whether the flow is upstream or downstream controlled.
* The staggered computational grid means there is always some interpolation occurring when projecting levels and velocities to the flux line. This will cause significant uncertainty in areas of sudden topographic change such as at an embankment. It is also the reason that two similar lines side by side may provide different outputs.
* Highly variable/complex flows, which are usually associated with highly variable topography, will add to the uncertainty when interpolating to the flux line.

The general recommendation is to use a lot of 2d_po features with reasonably small <tt>Time Series Output Interval</tt> to achieve better accuracy, they won't slow the simulation down. In case of flux lines, the 2d_po lines are using the actual computed fluxes across each cell face and will be an exact representation of the flow being calculated by the 2D solver. Post-processed fluxes should only be located in areas of little topographic change and even then, used as a rough guide. 

== Why are my TIF raster outputs not updated after a model rerun? ==
The GeoTIFF raster output is supported from the 2023-03 Release and onwards. It is considered an image by File Explorer and for some users, the 'Date' in file explorer defaults to show the 'Date Created' rather than the 'Date Modified'. If it appears that your results in the 'Results' folder (e.g. XMDF) are being updated however the results in the 'Results/grid' folder (e.g. TIF) aren't, this may be the reason. 
To confirm, turn on both 'Date Created' and 'Date Modified': 
[[File:TIF Date 01.png | 500px]] 
The 'Date Modified' column will show the date of the last model run: 
[[File:TIF Date 02.png | 500px]] 
 

== How do I apply high resolution (HR) output and what are the options? ==
See the [[TUFLOW_HR_Output | TUFLOW HR Output]] Wiki page for details. 
 

== Why are there differences between high resolution grid from TUFLOW and remapped outputs generated by the asc_to_asc utility? ==
Differences are expected between the direct HR output and the [[ASC_to_ASC#Remap | remap function]] in the asc_to_asc utility due to the different steps involved in the interpolation of water level results. 
The direct HR output is based on a triangulation of the water levels at the cell centres and corners. In comparison, the remap function involves two interpolation steps. The first is the interpolation from the cell centres and corners to the grid map output locations (by default, half the cell size). Then, the asc_to_asc utility interpolates the water level output to the target DEM grids with fine resolution: 
[[File:High-res-interpolation.jpg|600px]] 

The direct HR output tends to have better resolution in comparison to the remapped results. Due to the additional stages of interpolation involved with the remapped output, there is potential to lose definition in the results particularly in areas with steep slopes.

==How to create a maximum grid for datasets without direct maximum output from a TUFLOW run?==

For cumulative datasets without direct maximum output (e.g., cumulative infiltration, cumulative rainfall etc), the map output interval in the TCF needs to match the simulation end time in seconds. The end time output is when the maximum occurs for cumulative datasets, as it represents the cumulative results over the entire simulation duration.

For non-cumulative datasets without direct maximum output (e.g., Froude number, rainfall rate etc):
*Adjust the <tt>Map Output Interval</tt> in the TCF to a small value to ensure temporal variations are captured.
*Specify XMDF for <tt>Map Output Format</tt>.
*Identify the output ID number of the desired dataset within the .xmdf file:
<pre>"C:\TUFLOW\Releases\res_to_res\res_to_res_w64.exe" -xnfo results.xmdf</pre>
*Extract maximum values using the [[RES_to_RES#Introduction | RES to RES]] utility.
*Confirm the dataset's output ID number in the resulting (maxmax).xmdf file:
<pre>"C:\TUFLOW\Releases\res_to_res\res_to_res_w64.exe" -xnfo (maxmax).xmdf</pre>
*Convert the resulting dataset to a preferred grid format for GIS use using the [[TUFLOW_to_GIS#Introduction | TUFLOW to GIS]] utility.

:Note: For non-cumulative datasets, this method does not capture the true maximum, however can be close enough with a fine map output interval.

== Why are the values of the mapped water level results (h_max) lower than the terrain elevations (DEM_Z)? ==

As at TUFLOW Build 25.1.1, there is a current limitation for result outputs at shallow sheet flow cells on steeper terrain in sub-grid-scale (SGS) enabled models. Direct rainfall models are particularly prone to result in substantial extents of shallow sheet flow.

In TUFLOW HPC, the cell averaged depth is used computationally and by default in the depth outputs (see below).

Water level outputs, however, assume that water fills from the bottom of the SGS cell’s storage curve. This water level result is recorded at the cell centre, interpolated to the cell corners, and then the gridded outputs are triangulated from these points (orange line). In sheet flow situations when the volume in that cell is small, the cell-centered water level may sit below the terrain (blue dot on diagram) if the cell is steep enough. This can result in a water level results sitting below the DEM_Z, even when there is a positive depth result. Note this does not affect the simulation computationally, only how the water level results are output. 
[[File:SGS_Sheetflow_Waterlevel.jpg|600px]] 

== Why do the high-resolution (HR) mapped results have a significantly smaller extent than the standard (non-HR) grid outputs for a sub-grid-scale (SGS) direct rainfall HPC model? ==

The high resolution (HR) outputs are based on [[TUFLOW_HR_Output | intersecting]] the standard water level output results with the HR DEM_Z. Due to the [[TUFLOW_Output_Discussion#Why_are_the_values_of_the_mapped_water_level_results_.28h_max.29_lower_than_the_terrain_elevations_.28DEM_Z.29.3F | output limitation]] for sub-grid-scale (SGS) enabled models with shallow sheet flow (particularly direct rainfall models) on sufficiently steep terrain, water levels can be output lower than the DEM_Z terrain elevations. This is due to the assumption (for output purposes) that water fills the SGS cell’s storage curve from the bottom, which differs from the approach of cell-average depth used computationally in TUFLOW HPC and by default in depth outputs. When the HR water level results are processed, the full extent of the water level results are intersected with the HR DEM_Z and any water level results that are lower than the HR DEM_Z elevations due to the above effect are trimmed. The extent of the HR water level results are then used to trim the HR depth outputs.

{{Tips Navigation
|uplink=[[TUFLOW_Modelling_Guidance | Back to TUFLOW Modelling Guidance]]
}}

12da to from GIS

2026-01-21T01:37:03Z

Abrar.Alttahir:

= Introduction =
12da_to_from_GIS.exe converts .12da files from the 12D software [https://www.12d.com (www.12d.com)] to and from .mif and .shp formats.
The .12da file must be saved in ANSI encoding, not UTF.

When importing or exporting between 12D and GIS, specify the file to be translated:

* If the file has a .12da or .4da extension the program converts it to a .mif/.mid file.
* If the file has a .mif extension the program converts it to a .12da file.

When converting from a .12da file to a .mif file without any options 12da_to_from_gis.exe automatically creates a .mif/.mid file suitable for use by Read GIS Z Line.
This process is useful for importing 3D breaklines, for example a road design, directly into TUFLOW.

Several useful options are described in the table below. Particularly noteworthy are the following options:
* -xs: generates a TUFLOW 1D cross-section database from a 12D DTM. 

This method is preferable to manual cross section extraction and more accurate than extracting from a grid based DTM, for example Vertical Mapper or Spatial Analyst, because it only samples points where the DTM triangle sides intersect the cross-section line. This limits the number of points in each cross section profile and improves accuracy.

* -zln: useful when river bathymetry in the DTM is poor because aerial survey data are inaccurate where water or dense vegetation is present. 

This option allows a section to be carved through the DTM along the river based on a cross section survey.

=Options (switches)=
'''TABLE 1: 12da_to_from_GIS Options (Switches)'''
{| align="center" class="wikitable"

! Switch
! width=85% | Description
|-
!colspan="2" style="background-color:#005581; font-weight:bold; color:white;"| When converting from a GIS file to a .12da file
|-
|"-zln"|| When converting a .mif file to .12da file, if zln is specified polylines are interpreted in the same manner as a TUFLOW 3D breakline (see Read GIS Z Line) such that any points snapped to the polyline are used to set the elevations of any vertices along the polyline that do not have points snapped to them. The first attribute in the .mif file must be the Elevation. (Note, when converting in reverse from a .12da file to a .mif file without any options, 12da_to_from_mif automatically creates a .mif/.mid file suitable for use by Read GIS Z Line.) 
This is useful for creating 3D polylines for 12D where an elevation does not exist at a vertice. It is particularly useful where a river’s bathymetry in a DTM is being created from cross-section surveys, and the DTM operator wishes to use the elevations at the cross-section survey points, but needs to put more shape into the breaklines being digitised between the cross-sections so as to carve out the river’s bathymetry into the DTM. Using 12da_to_from_mid with the zln option will interpolate elevations at every string vertice, something that 12D does not offer (as far as we know!).
|-
!colspan="2" style="background-color:#005581; font-weight:bold; color:white;"| When converting from a .12da file to a GIS file
|-
|"-hip"|| When converting a .12da file to a .mif file will include any 12D hipdata polylines.
|-
|"-xs"|| When converting a .12da file to a .mif file creates a TUFLOW cross-section database (ie. one .csv file per 3D polyline) and a 1d_xs layer (1d_tab or 1d_xs format – see Read GIS Table Links and Section 4.6.3). This is used to extract 1D cross-sections from a DTM for use in TUFLOW. Cross-sections generated this way can also be viewed and edited in the SMS TUFLOW Interface. 
The process to create the cross-sections from a 12D DTM is as follows: 
*Digitise the location of cross-section lines either in a GIS or in 12D. If in a GIS, export the layer out as a .mif file and then run 12da_to_from_gis to convert the .mif file into a .12da file. For example use: 
:<tt>12da_to_from_gis.exe xs.mif</tt> 
:to generate a file called xs.12da. Import the .12da file into 12D. 
*In 12D drape the cross-section polylines over the DTM (your 12D operator should know how to do this!) to create 3D polylines with vertices where the cross-section line intersects a DTM triangle edge.
*Export the 3D draped polylines from 12D as a .12da file.
*Run 12da_to_from_gis using the -xs option. For example: 
:<tt>12da_to_from_gis.exe -xs -shp xs_draped.12da</tt> 
:This will produce a 1d_xs layer in shapefile format ready for use by TUFLOW and a .csv file for each cross-section line. Keep the 1d_xs layer and the .csv files in the same folder in the event that you move them elsewhere. 
Using the xsGenerator.exe utility, you can also assign material values to each of the cross-section points (see [[xsGenerator | this page]]).



|}
= Examples =
<tt>12da_to_from_gis.exe -mif road_breaklines.12da</tt> 
creates .mif/.mid files of the 2D and 3D breaklines in the file road_breaklines.12da. The .mif/.mid files can be directly used by Read GIS Z Line.

<tt>12da_to_from_gis.exe 2d_hx_lines.mif</tt> 
creates a file 2d_hx_lines.mif.12da. Import this file into 12D then drape these lines over the DTM and export the file, say as 2d_hx_lines_draped.12da, then execute the following:

<tt>12da_to_from_gis.exe 2d_hx_lines_draped.12da</tt>

This creates 3D breakline .mif/.mid files of the TUFLOW HX lines that can be used to ensure the 2D HX cells are set to the exact elevations along the HX lines by using the below command in the .tgc file.: 
<tt>Read GIS Z Line THICK == mi\2d_hx_lines_draped.12da.mif </tt>



=Frequently Asked Questions (FAQ)=
==Why are the SHP or MIF files created by the 12da_to_from_GIS utility empty?==
The reason for empty output files may be that 12D (v10 or later) produces output files in UTF-16 encoding by default. This encoding is not compatible with the utility and can lead to empty SHP or MIF outputs. The .12da can be exported from 12D into ANSI format, or readily converted in text editor from UTF16 to ANSI making it compatible with TUFLOW and TUFLOW utilities. Instructions for both methods are below. 
In 12D, when exporting the TIN as a .12da file, make sure to select the "[Ansi format]" 
[[File:12da_TIN_export_screenshot.JPG|500px]] 
This change will be highlighted at the bottom of the Write 12da pop up box. See figure below. 
[[File:12da_TIN_export_screenshot_ANSI_Format_set.JPG|500px]] 

Alternatively, an existing .12da file can easily be converted to ANSI encoding in Notepad++, under the Encoding menu: 
[[File:Convert ANSI.jpg|500px]]

{{Tips Navigation
|uplink=[[TUFLOW_Utilities | Back to TUFLOW Utilities]]
}}

12da to from GIS

2026-01-21T01:31:47Z

Abrar.Alttahir: /* Options (switches) */

= Introduction =
12da_to_from_GIS.exe converts .12da files from the 12D software [https://www.12d.com (www.12d.com)] to and from .mif and .shp formats.
The .12da file must be saved in ANSI encoding, not UTF.

When importing or exporting between 12D and GIS, specify the file to be translated:

* If the file has a .12da or .4da extension the program converts it to a .mif/.mid file.
* If the file has a .mif extension the program converts it to a .12da file.

When converting from a .12da file to a .mif file without any options 12da_to_from_gis.exe automatically creates a .mif/.mid file suitable for use by Read GIS Z Line.
This process is useful for importing 3D breaklines, for example a road design, directly into TUFLOW.

Several useful options are described in the table below. Particularly noteworthy are the following options:
* -xs: generates a TUFLOW 1D cross-section database from a 12D DTM. 

This method is preferable to manual cross section extraction and more accurate than extracting from a grid based DTM, for example Vertical Mapper or Spatial Analyst, because it only samples points where the DTM triangle sides intersect the cross-section line. This limits the number of points in each cross section profile and improves accuracy.

* -zln: useful when river bathymetry in the DTM is poor because aerial survey data are inaccurate where water or dense vegetation is present. 

This option allows a section to be carved through the DTM along the river based on a cross section survey.

=Options (switches)=
'''TABLE 1: 12da_to_from_GIS Options (Switches)'''
{| align="center" class="wikitable"

! Switch
! width=85% | Description
|-
!colspan="2" style="background-color:#005581; font-weight:bold; color:white;"| When converting from a GIS file to a .12da file
|-
|"-zln"|| When converting a .mif file to .12da file, if zln is specified polylines are interpreted in the same manner as a TUFLOW 3D breakline (see Read GIS Z Line) such that any points snapped to the polyline are used to set the elevations of any vertices along the polyline that do not have points snapped to them. The first attribute in the .mif file must be the Elevation. (Note, when converting in reverse from a .12da file to a .mif file without any options, 12da_to_from_mif automatically creates a .mif/.mid file suitable for use by Read GIS Z Line.) 
This is useful for creating 3D polylines for 12D where an elevation does not exist at a vertice. It is particularly useful where a river’s bathymetry in a DTM is being created from cross-section surveys, and the DTM operator wishes to use the elevations at the cross-section survey points, but needs to put more shape into the breaklines being digitised between the cross-sections so as to carve out the river’s bathymetry into the DTM. Using 12da_to_from_mid with the zln option will interpolate elevations at every string vertice, something that 12D does not offer (as far as we know!).
|-
!colspan="2" style="background-color:#005581; font-weight:bold; color:white;"| When converting from a .12da file to a GIS file
|-
|"-hip"|| When converting a .12da file to a .mif file will include any 12D hipdata polylines.
|-
|"-xs"|| When converting a .12da file to a .mif file creates a TUFLOW cross-section database (ie. one .csv file per 3D polyline) and a 1d_xs layer (1d_tab or 1d_xs format – see Read GIS Table Links and Section 4.6.3). This is used to extract 1D cross-sections from a DTM for use in TUFLOW. Cross-sections generated this way can also be viewed and edited in the SMS TUFLOW Interface. 
The process to create the cross-sections from a 12D DTM is as follows: 
*Digitise the location of cross-section lines either in a GIS or in 12D. If in a GIS, export the layer out as a .mif file and then run 12da_to_from_gis to convert the .mif file into a .12da file. For example use: 
:<tt>12da_to_from_gis.exe xs.mif</tt> 
:to generate a file called xs.12da. Import the .12da file into 12D. 
*In 12D drape the cross-section polylines over the DTM (your 12D operator should know how to do this!) to create 3D polylines with vertices where the cross-section line intersects a DTM triangle edge.
*Export the 3D draped polylines from 12D as a .12da file.
*Run 12da_to_from_gis using the -xs option. For example: 
:<tt>12da_to_from_gis.exe -xs -shp xs_draped.12da</tt> 
:This will produce a 1d_xs layer in shapefile format ready for use by TUFLOW and a .csv file for each cross-section line. Keep the 1d_xs layer and the .csv files in the same folder in the event that you move them elsewhere. 
Using the xsGenerator.exe utility, you can also assign material values to each of the cross-section points (see [[xsGenerator | this page]]).




= Examples =
<tt>12da_to_from_gis.exe -mif road_breaklines.12da</tt> 
creates .mif/.mid files of the 2D and 3D breaklines in the file road_breaklines.12da. The .mif/.mid files can be directly used by Read GIS Z Line.

<tt>12da_to_from_gis.exe 2d_hx_lines.mif</tt> 
creates a file 2d_hx_lines.mif.12da. Import this file into 12D then drape these lines over the DTM and export the file, say as 2d_hx_lines_draped.12da, then execute the following:

<tt>12da_to_from_gis.exe 2d_hx_lines_draped.12da</tt>

This creates 3D breakline .mif/.mid files of the TUFLOW HX lines that can be used to ensure the 2D HX cells are set to the exact elevations along the HX lines by using the below command in the .tgc file.: 
<tt>Read GIS Z Line THICK == mi\2d_hx_lines_draped.12da.mif </tt>



=Frequently Asked Questions (FAQ)=
==Why are the SHP or MIF files created by the 12da_to_from_GIS utility empty?==
The reason for empty output files may be that 12D (v10 or later) produces output files in UTF-16 encoding by default. This encoding is not compatible with the utility and can lead to empty SHP or MIF outputs. The .12da can be exported from 12D into ANSI format, or readily converted in text editor from UTF16 to ANSI making it compatible with TUFLOW and TUFLOW utilities. Instructions for both methods are below. 
In 12D, when exporting the TIN as a .12da file, make sure to select the "[Ansi format]" 
[[File:12da_TIN_export_screenshot.JPG|500px]] 
This change will be highlighted at the bottom of the Write 12da pop up box. See figure below. 
[[File:12da_TIN_export_screenshot_ANSI_Format_set.JPG|500px]] 

Alternatively, an existing .12da file can easily be converted to ANSI encoding in Notepad++, under the Encoding menu: 
[[File:Convert ANSI.jpg|500px]]

{{Tips Navigation
|uplink=[[TUFLOW_Utilities | Back to TUFLOW Utilities]]
}}

12da to from GIS

2026-01-21T01:29:10Z

Abrar.Alttahir: /* Examples */

= Introduction =
12da_to_from_GIS.exe converts .12da files from the 12D software [https://www.12d.com (www.12d.com)] to and from .mif and .shp formats.
The .12da file must be saved in ANSI encoding, not UTF.

When importing or exporting between 12D and GIS, specify the file to be translated:

* If the file has a .12da or .4da extension the program converts it to a .mif/.mid file.
* If the file has a .mif extension the program converts it to a .12da file.

When converting from a .12da file to a .mif file without any options 12da_to_from_gis.exe automatically creates a .mif/.mid file suitable for use by Read GIS Z Line.
This process is useful for importing 3D breaklines, for example a road design, directly into TUFLOW.

Several useful options are described in the table below. Particularly noteworthy are the following options:
* -xs: generates a TUFLOW 1D cross-section database from a 12D DTM. 

This method is preferable to manual cross section extraction and more accurate than extracting from a grid based DTM, for example Vertical Mapper or Spatial Analyst, because it only samples points where the DTM triangle sides intersect the cross-section line. This limits the number of points in each cross section profile and improves accuracy.

* -zln: useful when river bathymetry in the DTM is poor because aerial survey data are inaccurate where water or dense vegetation is present. 

This option allows a section to be carved through the DTM along the river based on a cross section survey.

=Options (switches)=
'''TABLE 1: 12da_to_from_GIS Options (Switches)'''
{| align="center" class="wikitable"

! Switch
! width=85% | Description
|-
!colspan="2" style="background-color:#005581; font-weight:bold; color:white;"| When converting from a GIS file to a .12da file
|-
|"-zln"|| When converting a .mif file to .12da file, if zln is specified polylines are interpreted in the same manner as a TUFLOW 3D breakline (see Read GIS Z Line) such that any points snapped to the polyline are used to set the elevations of any vertices along the polyline that do not have points snapped to them. The first attribute in the .mif file must be the Elevation. (Note, when converting in reverse from a .12da file to a .mif file without any options, 12da_to_from_mif automatically creates a .mif/.mid file suitable for use by Read GIS Z Line.) 
This is useful for creating 3D polylines for 12D where an elevation does not exist at a vertice. It is particularly useful where a river’s bathymetry in a DTM is being created from cross-section surveys, and the DTM operator wishes to use the elevations at the cross-section survey points, but needs to put more shape into the breaklines being digitised between the cross-sections so as to carve out the river’s bathymetry into the DTM. Using 12da_to_from_mid with the zln option will interpolate elevations at every string vertice, something that 12D does not offer (as far as we know!).
|-
!colspan="2" style="background-color:#005581; font-weight:bold; color:white;"| When converting from a .12da file to a GIS file
|-
|"-hip"|| When converting a .12da file to a .mif file will include any 12D hipdata polylines.
|-
|"-xs"|| When converting a .12da file to a .mif file creates a TUFLOW cross-section database (ie. one .csv file per 3D polyline) and a 1d_xs layer (1d_tab or 1d_xs format – see Read GIS Table Links and Section 4.6.3). This is used to extract 1D cross-sections from a DTM for use in TUFLOW. Cross-sections generated this way can also be viewed and edited in the SMS TUFLOW Interface. 
The process to create the cross-sections from a 12D DTM is as follows: 
*Digitise the location of cross-section lines either in a GIS or in 12D. If in a GIS, export the layer out as a .mif file and then run 12da_to_from_gis to convert the .mif file into a .12da file. For example use: 
:<tt>12da_to_from_gis.exe xs.mif</tt> 
:to generate a file called xs.12da. Import the .12da file into 12D. 
*In 12D drape the cross-section polylines over the DTM (your 12D operator should know how to do this!) to create 3D polylines with vertices where the cross-section line intersects a DTM triangle edge.
*Export the 3D draped polylines from 12D as a .12da file.
*Run 12da_to_from_gis using the -xs option. For example: 
:<tt>12da_to_from_gis.exe -xs -shp xs_draped.12da</tt> 
:This will produce a 1d_xs layer in shapefile format ready for use by TUFLOW and a .csv file for each cross-section line. Keep the 1d_xs layer and the .csv files in the same folder in the event that you move them elsewhere. 
Using the xsGenerator.exe utility, you can also assign material values to each of the cross-section points (see [[xsGenerator | this page]]).
|-
|"-out"|| '''UNDER CONSTRUCTION''' 
Specifies the output file name when creating a new file. For example: 
<tt>12da_to_from_gis.exe -out output_file.12da <Lines>.mif</tt> 
Will create the file with the chosen output name instead of the default.
|-
|"-model"|| '''UNDER CONSTRUCTION''' 
When converting to a .12da file, sets the model name in the output. For example: 
<tt>12da_to_from_gis.exe -model 12d_Model_Name <Lines>.mif</tt> 
Will embed the specified 12d model name into the created .12da file.
|}

= Examples =
<tt>12da_to_from_gis.exe -mif road_breaklines.12da</tt> 
creates .mif/.mid files of the 2D and 3D breaklines in the file road_breaklines.12da. The .mif/.mid files can be directly used by Read GIS Z Line.

<tt>12da_to_from_gis.exe 2d_hx_lines.mif</tt> 
creates a file 2d_hx_lines.mif.12da. Import this file into 12D then drape these lines over the DTM and export the file, say as 2d_hx_lines_draped.12da, then execute the following:

<tt>12da_to_from_gis.exe 2d_hx_lines_draped.12da</tt>

This creates 3D breakline .mif/.mid files of the TUFLOW HX lines that can be used to ensure the 2D HX cells are set to the exact elevations along the HX lines by using the below command in the .tgc file.: 
<tt>Read GIS Z Line THICK == mi\2d_hx_lines_draped.12da.mif </tt>



=Frequently Asked Questions (FAQ)=
==Why are the SHP or MIF files created by the 12da_to_from_GIS utility empty?==
The reason for empty output files may be that 12D (v10 or later) produces output files in UTF-16 encoding by default. This encoding is not compatible with the utility and can lead to empty SHP or MIF outputs. The .12da can be exported from 12D into ANSI format, or readily converted in text editor from UTF16 to ANSI making it compatible with TUFLOW and TUFLOW utilities. Instructions for both methods are below. 
In 12D, when exporting the TIN as a .12da file, make sure to select the "[Ansi format]" 
[[File:12da_TIN_export_screenshot.JPG|500px]] 
This change will be highlighted at the bottom of the Write 12da pop up box. See figure below. 
[[File:12da_TIN_export_screenshot_ANSI_Format_set.JPG|500px]] 

Alternatively, an existing .12da file can easily be converted to ANSI encoding in Notepad++, under the Encoding menu: 
[[File:Convert ANSI.jpg|500px]]

{{Tips Navigation
|uplink=[[TUFLOW_Utilities | Back to TUFLOW Utilities]]
}}

TUFLOW 1D Channels and Hydraulic Structures

2026-01-21T01:26:59Z

Abrar.Alttahir: /* How can a flow splitter with a nib wall be modelled? */

=Introduction=
The objective of the following pages is to supplement the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW Manual] and provide additional modelling guidance on 1D hydraulic channels.
=1D Channel Types=
1d_nwk channels represent open channels, hydraulic structures, operational structures and other flow controls. A channel is either digitised as a line or point with the relevant hydraulic properties entered into the appropriate GIS attributes, details for this can be found within the links below or the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW Manual].
==Open Channels==
{| class="wikitable" width="50%"

! style="background-color:#005581; font-weight:bold; color:white;"| Channel Type
! style="background-color:#005581; font-weight:bold; color:white;" width=75%| Description of page contents
|-
| [[ 1D_Open_Channels | Open Channels]]|| This page contains information on basic commands, error checking and common check files used.
|}

==Structures==
The table below contains a complete list of 1D structures available within TUFLOW, including logic control structures. 
{| class="wikitable" width="50%"

! style="background-color:#005581; font-weight:bold; color:white;"| Channel Type
! style="background-color:#005581; font-weight:bold; color:white;" width=75%| Description of page contents
|-
| [[1D_Culverts | Culverts]]|| 1D-2D connections, flow regimes, operational control and common check files used.
|-
| [[1D_Bridges | Bridges]]|| Loss theory, irregular shaped bridges and common check files used.
|-
| [[1D_Weirs | Weirs]]|| Weir types
|-
| [[ 1D_Pumps | Pumps]]|| Pump attributes, 2D-2D & 1D-2D configurations, Estry setup, depth-discharge database and 1D results file.
|-
| [[1D_Pits | Pits]]|| Pit inlet types, depth-discharge data sources, modelling advice.
|-
| [[1D_Manholes | Manholes]]|| Losses and storage chambers.
|-
|[[1D Syphons|Syphons]]
|1D configuration, losses.
|-
|}

= Common Questions Answered (FAQ)=
== What is the difference between 1d_pit and 1d_nwk layer for pits? ==
The 1d_pit layer is a newer and simplified version of the 1d_nwk layer for pits. 
=== 1d_pit ===
* Assumes default values for any of missing attributes that are described for the 1d_nwk pits layers.
* Does not have invert attributes. The upstream invert is assumed to be the ZC elevation of the connected 2D SX cell, as such the "L" flag is not supported. The downstream invert needs to retrieve its value from the connected 1d_nwk channel.
* The nodal area assumes the default, the downstream pit channel node has its nodal area automatically assigned based on the connected 1d_nwk channel segments that have the UCS attribute set to “true”. The applied nodal area can be confirmed in the .eof file. 
* Virtual pipes can only be setup with the 1d_pit layer.
=== 1d_nwk ===
* Used for models that needs to deviate from the default approaches.

== How can I read approach-capture flow curves for on-grade pits into TUFLOW? ==
TUFLOW currently doesn't support the use of approach-capture flow curves directly. Manning's equation calculations can be used to convert on-grade approach flow information to a water depth at the pit. For example, [https://www.arr-software.org/arrdocs.html Australian Rainfall and Runoff 2019] (Engineers Australia) (Book 9, Chapter 5, Section 5.6.1) recommends using the modified Mannings equation, the Izzard's equation, for gutter and roadway flow.

The pit depth-flow curve relationship can be derived as follows:
*Select a few representative slope increments for the project area and conduct a Manning's equation calculation for each slope to calculate the gutter flow rates for incrementally increasing flow depths.
*Multiply the calculated flows by the % value in the on grade curve. This turns the % capture vs. flow curve into a depth vs. flow relationship.
*Repeat for different gutter shapes

== Should I model culverts in the 1D or 2D domain? ==
This largely depends on the size of the culvert in comparison with the cell size. Most commonly culverts are modelled within the 1D domain, however, with more computational power given by GPU devices, cell sizes are getting smaller and it might be beneficial to model bigger culverts in 2D. If the culvert is an important aspect of the study, it is always recommended to model it in number of ways by either using different methods and/or different software and the afflux is cross-checked against a desktop analysis and hand calculations. None will be 100% right, but the intent here is to establish a verification of the preferred approach. 
=== 2D only approach ===
* Benefits:
** Good stability and improved hydraulic behaviour at the culvert entrance/exit.
** The expansion loss (exit loss) can be explicitly handled in 2D provided the 2D cell resolution is sufficiently fine to model the expansion of flow downstream of the culvert.
* Challenges:
** The contraction loss (entry loss), which is related to the expansion of water after the vena-contracta and forms inside at the culvert inlet, won't be picked up well and some additional energy loss (form loss) might be needed to cover this.
** Side and soffit wall friction are not modelled unless Manning's n varies with depth, i.e. increase Manning's n with depth to account for the missing Manning's n on the side and top surfaces.
** The vertical walls not only create extra friction but also straightens the flow in the direction of the wall. Thin breaklines can be used to represent these walls in 2D, but it is likely to cause saw-tooth effect if sub-grid sampling (SGS) is not used, i.e. extra numerical head loss, if there are too few cells between the walls. SGS is recommended in this case.
** Flow overtopping can be represented to some extent by assuming 100% blockage of layer 2 in a 2d_lfcsh, but the flow upstream and downstream before the culvert is overtopped is hard to model.

=== 2D-1D-2D approach ===
* Benefits:
** A more appropriate approach where the 2D cell size is greater than around half the total culvert width.
** Contraction losses (entry losses) are handled better.
** Flow overtopping can be modelled in 2D.
* Challenges:
** Expansion losses (exit losses) are very dependent on the 2D cell resolution. A 2D cell size much larger than the culvert width will not reproduce the expansion losses very well (even with SGS) and the culvert's exit loss needs to cover this. A finer 2D cell size (several or more cells across the culvert) will reproduce the expansion losses much better and the culvert's exit loss need to be reduced to compensate it. This usually only happens for large 1D culverts with high velocities as there can be losses duplicated in the 2D on the exit side as the 2D flow expands (i.e. duplication of the exit/expansion loss). The latest release has a new feature to automatically adjust 1D culvert losses based on the 2D approach/departure velocities as what happens with a 1D-1D-1D arrangement. See this [https://downloads.tuflow.com/_archive/Publications/Modelling%20of%20Bends%20and%20Hydraulic%20Structures%20in%20a%202D%20Scheme,%20Syme,%202001.pdf paper] for more information.
** HX connections may cause instability, especially with a skewed culvert outlet, but can produce better velocity patterns downstream of the culvert to model the expansion losses, which will be occurring in the 2D domain.
** Given that the SX connection applies the flow going out of the 1D culvert as a source term without momentum, it is difficult to completely prevent the water from piling up. If required, wingwalls can be modelled as thin breaklines to help guide the water away. Using the SX boundary Z flag lowers other SX cells below the 1D culvert invert level and it can mitigate the water from piling up.

== What entry/exit loss and contraction coefficients should I use for 1D culverts? ==
We don’t provide hard recommendations on the exit and entry losses to use for culverts as we have found different organisations around the world, typically government, have their own guidelines for different types of inlets configurations and require these to be used, for example, the [https://www.ipwea-qnt.com/products-resources/qudm/ Queensland Urban Drainage Manual] (QUDM). However, it is very important to understand how losses are applied and that different 1D solvers may treat them differently. For cross-checking your results from any hydraulic modelling software, a simple calculation applying the entry and exit losses (allowing for any automatic adjustments as discussed below) to the computed head (V2/2g), plus allowing any surface roughness losses (Manning's equation) for longer culverts, is the best practice for culverts flowing in a sub-critical flow condition (i.e. downstream controlled flow). 

For the entrance loss values, the approach should be to use values as quoted in the literature or guidelines for the inlet shape and design unless there is evidence to use another value (e.g. comparison with reliable calibration data would indicate different energy losses).

For the exit loss a value of 1.0 is recommended in nearly all situations provided losses are being adjusted every timestep. A value of 1.0 with adjusted losses is derived from the fluid flow physics (momentum or energy conservation) for expanding flow and will be the most precise approach for modelling exit losses or to apply a bend loss to the approach/departure channel if these are modelled in 1D.

Occasionally there are situations where non-standard entrance and exit loss values are needed. A good example is if the approach or departure flow is skewed to the culvert direction. In these situations there may also be a significant bend (energy) loss occurring as the water changes direction entering or leaving the structure. To account for this the modeller may need to increase the entrance and/or exit loss values. 

By default, TUFLOW adjusts the entrance and exit losses of 1D structures flowing sub-critical every timestep based on the approach/departure velocities for 1D-1D-1D and with a new feature in the 2020-10-AA build for 2D-1D-2D. The entrance losses are adjusted based on an empirical relationship from flume testing whilst the exit loss equation is theoretically derived as mentioned above - refer to the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW Manual]. The modeller should be familiar with the approach taken by the software they are using as some software either don't adjust for approach/departure velocities at structures (will overestimate losses using standard values) and some may apply a limiting loss thereby not allowing the losses to sufficiently reduce.

TUFLOW, by default, allows the losses to reduce to effectively a zero loss coefficient (i.e. 0.0001). A zero loss occurs where the approach and departure velocity is the same as the structure velocity. For example, a clear-spanning bridge over a concrete lined channel with the water level below the bridge deck will experience no energy losses until the bridge deck is surcharged so if your software is applying unadjusted or limited energy loss coefficients there will be an unrealistic energy loss at the structure for flow below the bridge deck. For culverts, in most cases there will be some losses as it is rare that the channel is of identical shape and slope to the culvert with usually the culvert being more constrictive and therefore a higher velocity so the adjusted coefficients are nearly always non-zero. At the other extreme is flow from or into a near still body of water (e.g. a lake or the ocean). In this situation the loss coefficient(s) will not be reduced and the maximum energy loss possible should occur.

If the default adjust losses approach is used (<tt>Structure Losses</tt> <tt>==</tt><tt> ADJUST</tt>) the recommendations are to use industry guidelines for the entrance loss coefficient based on the shape/design of the inlet (these coefficients are typically based on a near zero approach velocity), and to use 1.0 for the exit loss coefficient. This applies to 1D culverts connected to 1D channels. The adjusted entrance and exit losses can be viewed over time in the _TSL layer, refer to the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW Manual]. 

Since the 2020-10-AA release, TUFLOW has a new (beta functionality) to have the losses automatically adjusted for linked 1D culverts and other structures connected to 2D domains through SX links (<tt>Structure Losses SX</tt> <tt>==</tt><tt> ADJUST</tt>), see Section 6.2 from the [https://downloads.tuflow.com/TUFLOW/Releases/2020-10/TUFLOW%20Release%20Notes.2020-10-AA.pdf 2020-10 Release Notes]. 

The other values to consider for modelling culverts are the inlet contraction coefficients used when the flow is upstream controlled flow should this occur. Typically the TUFLOW default values for these values should be used unless the inlet shape and design indicates otherwise. 

== Should I model pits as R or Q type? ==
Q pits are recommended for modelling kerb inlets, catchpits, drains, etc., provided that appropriate y-Q curves are available. TUFLOW automatically extends the y-Q curve for higher depths based on the orifice equation. Under downstream controlled flow regime it reverts to a drowned condition. 
When using the R pit, whilst the pit is free-falling into the pipe system below (most of the time until the system surcharges), entrance/exit losses don't apply, because the flow is inlet controlled. The R pit would be using the weir equation for unsubmerged flow or orifice flow for submerged flow and could be a poor representation of the pit flow behaviour, especially if the pit also has a grate and other non-rectangular characteristics.

== How to model pipe network with some pipe data missing? ==
In situations where the complete pipe network is not modelled there are numerous common approaches adopted by modellers:
* Use direct rainfall boundary conditions, model all inlets, only model the larger pipes in the pipe network. Where a small pipe is being omitted, connect the flows from the inlet to the main pipe network using virtual pipes functionality. Only the location of the inlet pits are needed, they can be approximated and sometimes lumped representation of the pits. This approach is especially good if the pipe network does not surcharge back to the overland flow anywhere or only in a minor way.
* Use lumped hydrology inflows. Only model the main pipe network and connected inlets (excluding the smaller features). Apply the flows directly to the pits either with:
** 1d_bc QT polygon boundaries with a P flag. This automatically distributes the total flow to all the pits within the polygon.
** 2d_sa boundaries with a "Read GIS SA PITS" command. The flow is applied to the ground surface rather than directly into the manhole. Some of the flow will surcharge out of the pits if the downstream pipe capacity is exceeded.

== How can I get more inflow into the pipe system? ==
A shallow depth can occur in the pit inlet cell and underestimate the depth at the entrance to the pit, restricting the amount of flow entering the pit. This can happen specifically for larger cell sizes. Different options to capture more water from 2D cells into the pits are available based on the TUFLOW engine. Both approaches aim to account for changes in ground elevation that occur at a finer scale than the 2D cell resolution:
*TUFLOW HPC - use Sub-Grid Sampling (SGS) - this approach samples underlying DEM with finer resolution and represents the true topography more accurately guiding water into the pits.
*TUFLOW Classic - use "Pit Default Road Crossfall" command to increase the depth at Q pits based on the crossfall slope of the road cross section. TUFLOW calculates the water depth that is used by the Pit Inlet Database using an adjacent side triangle depth approach, instead of the actual cell flow depth.

== How is flow through the culvert calculated? ==
Computationally, information is passed between the 1D and 2D on the half timestep:
* Flow through the culvert is calculated based on the water levels at the inlet/outlet and the assigned flow regime. The volume is extracted/added to the 2D boundary cells on the half timestep.
* On the next full timestep the 1D and 2D shallow water equations are applied to move water through their respective domains.

== What should I do if 1D pump doesn't convey as much flow as expected and/or seems to be unstable? ==
A couple of things can be checked:
* The inlet of the pipe has to be fully submerged, otherwise the pump will shut down and there will be no flow through the pump.
* Add extra storage to the node upstream of the pump with 1d_na table or 1d_nwk type NODE through ANA attribute.
* Try reasonably smaller 1D timestep.
* If using non-operational pump (P), check if the depth discharge relationship is appropriate. If outlet of the pipe is lower than inlet, the pump will always try to pump at full capacity.
* Consider using operational pump (PO), where the pump would switch off if the water level upstream gets below the pump soffit.

== How BB bridges automatically switch between pressure flow and being drowned out? ==
TUFLOW considers the top of the XZ or HW table as the soffit level. If the downstream water level exceeds the soffit level, TUFLOW does the pressure flow calculation using default K = 1.56. It compares the velocity calculated by the pressure flow equation and by the form loss calculation based on LC table and uses the smaller one.
The _TSF and _TSL layers can be used to find out the regime/form loss values used for BB bridges:
*"P": pressure flow
*"D": ds water level > the soffit level, but it applies normal flow because the normal flow equation predicts smaller velocity
*" ": ds water level < the soffit level, normal flow

== Should I model bridges in the 1D or 2D Domain? ==
The recommended approach typically depends on the study objectives and if the channel upstream and downstream of the bridge is modelled in 1D or 2D. To preserve the momentum as accurately as possible the bridge should be modelled in the same dimension as the channel, e.g. 1d_nwk bridge if the channels is in 1D and 2d_bg or 2d_lfcsh if the channel is modelled in 2D. 
In 2D, the expansion/contraction losses are modelled based on the topography and don't need to be estimated as attributes as for 1D modelling. Also, for higher flows where the bridge is overtopped, 2D is preferable approach.

== What is the difference between downstream and upstream controlled flow? ==
Downstream control means a change in downstream water level will cause a change in upstream water level. Upstream control means the upstream water level is insensitive to the downstream water level and usually indicates the occurrence of supercritical flow.

== How do negative "M", "N" and "R" values deactivate 1D cross-sections? ==
When negative “M” values are used to deactivate 1D cross-sections, it changes the flow area, wetted perimeter, hydraulic radius, and the conveyance, while keeping the storage width unchanged. It is like inserting a thin plate at the middle of the channel, as shown in the figure below. This approach is preferred to retain the storage in the system but reduce the conveyance (e.g., off-stream storage not contributing to conveyance or sections either side of a constriction to generate better approach/departure velocities if using the Total Area approach). 
'''Note:''' The negative "M" value must be a negative of a valid material ID. 
 
<ol>
[[File:Section deactivation M.png | 200px]] 
</ol>
When using negative (-1) “N” (Manning’s n) or “R” (relative resistance factor) to deactivate the 1D cross-sections, it reduces both the flow area and the storage width, as shown in the figure below. 
 
<ol>
[[File:Section deactivation N R.png | 200px]]
</ol>

==Calculating a 1d_bc head-discharge curve==

As of the 2023-03-AD release, it is possible for TUFLOW to automatically create a Head-Discharge curve for 1d_bc HQ boundaries. For more information see Section 3.5 of the [https://docs.tuflow.com/classic-hpc/release/2023-03-AE/OneDSolv-1.html#Auto1DHQ-2 2023-03 Release Notes].

If using older releases, this can be calculated manually as the condition to be satisfied for a normal depth is the Manning’s equation. The HQ depth should be set up with elevation and the discharge calculated from Manning’s equation (including Area and Hydraulic Radius for each elevation point) and can be calculated in a few steps: 

<ol>
<li>Set the model up your ESTRY model using a dummy downstream level boundary (e.g. a HQ boundary with no name, an error will be reported however the required check file will have been produced, otherwise any boundary will work) and run the model in test model (-t) so that check files are produced.
<li>From the check folder, open the '''*_1d_ta_tables.csv''' file.
<li>Find the entry for the most downstream cross-section (directly upstream of the 1d_bc HQ point). The '''*_1d_ta_tables.csv''' will contain a table of hydraulic parameters. The K entry highlighted is the cross-section conveyance in m3/s and is calculated as: 

K = 1/n * A * R2/3 

Where
* A is the Eff Area
* R is the Radius
 
[[File:Ta tables example.png | 800px ]] 

<li>Determine a representative slope (S), this could be taken from the last two cross-sections.
<li>Calculate the downstream Discharge (this is the same as calculated discharge from Mannings equation): 

Q = K * SQRT(S) 

Where:
* S is a representative slope value for the downstream reach.
* K is obtained from the '''1d_ta_tables.csv'''

<li>Use the Elevation (Head) vs Discharge pairs to populate the HQ table.
</ol>

==Why is the maximum 1D water level higher than 2D water level?==
TUFLOW calculates storage at 1D nodes (including manholes) and calculates flux at pipe mid-sections. The storages at the manholes are saved in the format of ‘elevation vs nodal area’ tables, which can be reviewed in the .eof file: 
<ol>
[[File:1DpitHmax 1.png]] 
</ol>

The change in water volume in a manhole is calculated based on the inflow from the pit above (if there is one) and the inflow/outflow from the connected pipes. The change in water level is then calculated based on the change in volume and the nodal area at that elevation. 
<ol>
[[File:1DpitHmax 2.png | 300px ]] 
</ol>

TUFLOW extends the ‘elevation vs nodal area’ table by 5 m (the last 2 numbers in the table above), which allows for the calculation of pressurised pipe flow when a manhole is drowned. If the maximum 1D water level is higher than the DEM, that means the manhole was drowned and pressurised during the peak of the flood. 
<ol>
[[File:1DpitHmax 3.png | 300px ]] 
</ol>

==How to represent the hydraulic behavior at the outlet of a pipe, including headwalls and transitions to open channels? ==
In general, pipe outlet headwalls affect the exit loss at the pipe outlet, and this can be modeled by changing the 1d_nwk “ExitC_or_WSb” attribute - exit loss coefficient for outlet controlled flow. The default exit loss coefficient is 1.0, assuming there is no headwall. For more details on these factors, refer to the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW Manual].
More guidance on applying entry/loss and contraction coefficients is available [[TUFLOW_1D_Channels_and_Hydraulic_Structures#What_entry.2Fexit_loss_and_contraction_coefficients_should_I_use_for_1D_culverts.3F | here]].

Once the water is in the 2D domain, headwalls can also affect the local flow field. The modelled hydraulic behavior depends on the model cell size. If the model has grid size smaller than the length of the headwall, thin breaklines or 2d_zsh tin can be added to represent the geometry in the 2D model. If the grid size is larger than the headwall, it only calculates ‘cell-averaged’ velocity including the impact of the headwall.

==Calculating velocity and bed shear stress at a pipe outlet==
The velocity and bed shear stress reported in the 2D domain at a pipe outlet depends on the model cell size. If the cell size is small enough and any outlets structures (i.e. wingwalls) are properly represented by the 2d cell/face elevations, TUFLOW can reasonably represent the flow field at the pipe outlet and estimate the bed shear stress base on the manning’s equation. However, be aware that TUFLOW solves 2D depth-averaged shallow water equation, so there is limitation of using it to estimate bed shear stress around complex structures or geometry that could experience vertical acceleration or re-circulation.

== How do I model blockage for irregular shaped culverts or channels? ==
For irregular shaped culverts and channels (1d_nwk "I" type), it is possible to model blockage by using the Skew (in degrees) attribute in the 1d_xs layer. This reduces the effective flow width of the cross section by rotating it relative to the direction of flow and can be used to approximate reduced capacity.

A skew angle of 0 means the cross section is aligned with the flow. As the angle increases, the effective width is reduced. For example, a skew of 60 degrees results results in a 50% reduction (as cos(60) = 0.5). While this does reduce the effective width of the structure, it isn’t expected to see the same reduction to conveyance as conveyance depends on the Manning’s n value, flow area and hydraulic radius.

== Do calculations differ if I specify multiple culverts using the Number_of attribute, even if the total dimensions are the same as a single culvert? ==
Yes. This can slightly affect the modelling results.

The key difference between a single wide culvert and multiple culverts with the same total width is the wetted perimeter. Multiple culverts have a slightly larger wetted perimeter due to the internal walls, which increases friction in downstream-controlled regimes. While friction losses are generally small compared to entry and exit losses, the impact on hydraulic behaviour is usually minor.

However, it’s important to model the configuration that best represents the actual structure when assessing culvert performance.

== How do I model a scruffy dome? ==
A scruffy dome is a type of dome inlet screen installed over a drop inlet, commonly used in stormwater systems. These inlets can be modelled in TUFLOW using a 1D Q type pit with a depth–discharge curve that reflects the hydraulic performance of the dome.

[https://www.ipwea-qnt.com/products-resources/qudm/ QUDM] recommends using the lesser of the weir flow or orifice flow equations to define inlet capacity (see Section 7.5.4). A blockage factor is also recommended to account for potential debris build-up during flood events (see Table 7.5.1 Provision for blockage at kerb inlets). This factor may vary depending on local guidelines, so regional standards should be consulted.

Once the rating curve is calculated, it can be added to the pit inlet database and linked to the Q type pit.
 
<ol>
[[File:How_do_I_model_a_scruffy_dome_in_TUFLOW.png|300x300px]] 
</ol>

== How can trash screens be represented? ==
A trash screen is a physical barrier, typically made from bars or mesh, placed at the inlet or outlet of a culvert, pipe, or channel to prevent debris from entering or exiting the structure.

Trash screens can be represented by applying additional head losses and blockage effects in accordance with [https://www.ipwea-qnt.com/products-resources/qudm/ QUDM] guidance. The approach depends on the location of the screen and the degree of blockage.

Possible use cases include:

* Where the screen is attached directly to the culvert inlet or outlet, the losses are applied to the culvert itself.
* Where the screen is located upstream of a culvert inlet or downstream of a culvert outlet, it can be modelled as a short zero length CF, IF, or RF channel before (upstream) or after (downstream) the culvert.
* In all cases, the net flow area should be reduced to account for the bars and any debris blockage, either by adjusting the flow area directly or by using the pBlockage attribute.

For example, the image below could be represented as a rectangular shaped screen located upstream or downstream of a culvert, modelled as a short zero length RF (rectangular culvert with fixed losses) channel using a 1d_nwk GIS layer.
<ol>
[[File:Trash_screen_schematic.png|alt=|300x300px]]
</ol>





{{Tips Navigation
|uplink=[[ TUFLOW_Modelling_Guidance | Back to TUFLOW Modelling Guidance]]
}}

TUFLOW 1D Channels and Hydraulic Structures

2026-01-21T01:26:03Z

Abrar.Alttahir: /* How can fishway baffles in a culvert be modelled? */

=Introduction=
The objective of the following pages is to supplement the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW Manual] and provide additional modelling guidance on 1D hydraulic channels.
=1D Channel Types=
1d_nwk channels represent open channels, hydraulic structures, operational structures and other flow controls. A channel is either digitised as a line or point with the relevant hydraulic properties entered into the appropriate GIS attributes, details for this can be found within the links below or the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW Manual].
==Open Channels==
{| class="wikitable" width="50%"

! style="background-color:#005581; font-weight:bold; color:white;"| Channel Type
! style="background-color:#005581; font-weight:bold; color:white;" width=75%| Description of page contents
|-
| [[ 1D_Open_Channels | Open Channels]]|| This page contains information on basic commands, error checking and common check files used.
|}

==Structures==
The table below contains a complete list of 1D structures available within TUFLOW, including logic control structures. 
{| class="wikitable" width="50%"

! style="background-color:#005581; font-weight:bold; color:white;"| Channel Type
! style="background-color:#005581; font-weight:bold; color:white;" width=75%| Description of page contents
|-
| [[1D_Culverts | Culverts]]|| 1D-2D connections, flow regimes, operational control and common check files used.
|-
| [[1D_Bridges | Bridges]]|| Loss theory, irregular shaped bridges and common check files used.
|-
| [[1D_Weirs | Weirs]]|| Weir types
|-
| [[ 1D_Pumps | Pumps]]|| Pump attributes, 2D-2D & 1D-2D configurations, Estry setup, depth-discharge database and 1D results file.
|-
| [[1D_Pits | Pits]]|| Pit inlet types, depth-discharge data sources, modelling advice.
|-
| [[1D_Manholes | Manholes]]|| Losses and storage chambers.
|-
|[[1D Syphons|Syphons]]
|1D configuration, losses.
|-
|}

= Common Questions Answered (FAQ)=
== What is the difference between 1d_pit and 1d_nwk layer for pits? ==
The 1d_pit layer is a newer and simplified version of the 1d_nwk layer for pits. 
=== 1d_pit ===
* Assumes default values for any of missing attributes that are described for the 1d_nwk pits layers.
* Does not have invert attributes. The upstream invert is assumed to be the ZC elevation of the connected 2D SX cell, as such the "L" flag is not supported. The downstream invert needs to retrieve its value from the connected 1d_nwk channel.
* The nodal area assumes the default, the downstream pit channel node has its nodal area automatically assigned based on the connected 1d_nwk channel segments that have the UCS attribute set to “true”. The applied nodal area can be confirmed in the .eof file. 
* Virtual pipes can only be setup with the 1d_pit layer.
=== 1d_nwk ===
* Used for models that needs to deviate from the default approaches.

== How can I read approach-capture flow curves for on-grade pits into TUFLOW? ==
TUFLOW currently doesn't support the use of approach-capture flow curves directly. Manning's equation calculations can be used to convert on-grade approach flow information to a water depth at the pit. For example, [https://www.arr-software.org/arrdocs.html Australian Rainfall and Runoff 2019] (Engineers Australia) (Book 9, Chapter 5, Section 5.6.1) recommends using the modified Mannings equation, the Izzard's equation, for gutter and roadway flow.

The pit depth-flow curve relationship can be derived as follows:
*Select a few representative slope increments for the project area and conduct a Manning's equation calculation for each slope to calculate the gutter flow rates for incrementally increasing flow depths.
*Multiply the calculated flows by the % value in the on grade curve. This turns the % capture vs. flow curve into a depth vs. flow relationship.
*Repeat for different gutter shapes

== Should I model culverts in the 1D or 2D domain? ==
This largely depends on the size of the culvert in comparison with the cell size. Most commonly culverts are modelled within the 1D domain, however, with more computational power given by GPU devices, cell sizes are getting smaller and it might be beneficial to model bigger culverts in 2D. If the culvert is an important aspect of the study, it is always recommended to model it in number of ways by either using different methods and/or different software and the afflux is cross-checked against a desktop analysis and hand calculations. None will be 100% right, but the intent here is to establish a verification of the preferred approach. 
=== 2D only approach ===
* Benefits:
** Good stability and improved hydraulic behaviour at the culvert entrance/exit.
** The expansion loss (exit loss) can be explicitly handled in 2D provided the 2D cell resolution is sufficiently fine to model the expansion of flow downstream of the culvert.
* Challenges:
** The contraction loss (entry loss), which is related to the expansion of water after the vena-contracta and forms inside at the culvert inlet, won't be picked up well and some additional energy loss (form loss) might be needed to cover this.
** Side and soffit wall friction are not modelled unless Manning's n varies with depth, i.e. increase Manning's n with depth to account for the missing Manning's n on the side and top surfaces.
** The vertical walls not only create extra friction but also straightens the flow in the direction of the wall. Thin breaklines can be used to represent these walls in 2D, but it is likely to cause saw-tooth effect if sub-grid sampling (SGS) is not used, i.e. extra numerical head loss, if there are too few cells between the walls. SGS is recommended in this case.
** Flow overtopping can be represented to some extent by assuming 100% blockage of layer 2 in a 2d_lfcsh, but the flow upstream and downstream before the culvert is overtopped is hard to model.

=== 2D-1D-2D approach ===
* Benefits:
** A more appropriate approach where the 2D cell size is greater than around half the total culvert width.
** Contraction losses (entry losses) are handled better.
** Flow overtopping can be modelled in 2D.
* Challenges:
** Expansion losses (exit losses) are very dependent on the 2D cell resolution. A 2D cell size much larger than the culvert width will not reproduce the expansion losses very well (even with SGS) and the culvert's exit loss needs to cover this. A finer 2D cell size (several or more cells across the culvert) will reproduce the expansion losses much better and the culvert's exit loss need to be reduced to compensate it. This usually only happens for large 1D culverts with high velocities as there can be losses duplicated in the 2D on the exit side as the 2D flow expands (i.e. duplication of the exit/expansion loss). The latest release has a new feature to automatically adjust 1D culvert losses based on the 2D approach/departure velocities as what happens with a 1D-1D-1D arrangement. See this [https://downloads.tuflow.com/_archive/Publications/Modelling%20of%20Bends%20and%20Hydraulic%20Structures%20in%20a%202D%20Scheme,%20Syme,%202001.pdf paper] for more information.
** HX connections may cause instability, especially with a skewed culvert outlet, but can produce better velocity patterns downstream of the culvert to model the expansion losses, which will be occurring in the 2D domain.
** Given that the SX connection applies the flow going out of the 1D culvert as a source term without momentum, it is difficult to completely prevent the water from piling up. If required, wingwalls can be modelled as thin breaklines to help guide the water away. Using the SX boundary Z flag lowers other SX cells below the 1D culvert invert level and it can mitigate the water from piling up.

== What entry/exit loss and contraction coefficients should I use for 1D culverts? ==
We don’t provide hard recommendations on the exit and entry losses to use for culverts as we have found different organisations around the world, typically government, have their own guidelines for different types of inlets configurations and require these to be used, for example, the [https://www.ipwea-qnt.com/products-resources/qudm/ Queensland Urban Drainage Manual] (QUDM). However, it is very important to understand how losses are applied and that different 1D solvers may treat them differently. For cross-checking your results from any hydraulic modelling software, a simple calculation applying the entry and exit losses (allowing for any automatic adjustments as discussed below) to the computed head (V2/2g), plus allowing any surface roughness losses (Manning's equation) for longer culverts, is the best practice for culverts flowing in a sub-critical flow condition (i.e. downstream controlled flow). 

For the entrance loss values, the approach should be to use values as quoted in the literature or guidelines for the inlet shape and design unless there is evidence to use another value (e.g. comparison with reliable calibration data would indicate different energy losses).

For the exit loss a value of 1.0 is recommended in nearly all situations provided losses are being adjusted every timestep. A value of 1.0 with adjusted losses is derived from the fluid flow physics (momentum or energy conservation) for expanding flow and will be the most precise approach for modelling exit losses or to apply a bend loss to the approach/departure channel if these are modelled in 1D.

Occasionally there are situations where non-standard entrance and exit loss values are needed. A good example is if the approach or departure flow is skewed to the culvert direction. In these situations there may also be a significant bend (energy) loss occurring as the water changes direction entering or leaving the structure. To account for this the modeller may need to increase the entrance and/or exit loss values. 

By default, TUFLOW adjusts the entrance and exit losses of 1D structures flowing sub-critical every timestep based on the approach/departure velocities for 1D-1D-1D and with a new feature in the 2020-10-AA build for 2D-1D-2D. The entrance losses are adjusted based on an empirical relationship from flume testing whilst the exit loss equation is theoretically derived as mentioned above - refer to the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW Manual]. The modeller should be familiar with the approach taken by the software they are using as some software either don't adjust for approach/departure velocities at structures (will overestimate losses using standard values) and some may apply a limiting loss thereby not allowing the losses to sufficiently reduce.

TUFLOW, by default, allows the losses to reduce to effectively a zero loss coefficient (i.e. 0.0001). A zero loss occurs where the approach and departure velocity is the same as the structure velocity. For example, a clear-spanning bridge over a concrete lined channel with the water level below the bridge deck will experience no energy losses until the bridge deck is surcharged so if your software is applying unadjusted or limited energy loss coefficients there will be an unrealistic energy loss at the structure for flow below the bridge deck. For culverts, in most cases there will be some losses as it is rare that the channel is of identical shape and slope to the culvert with usually the culvert being more constrictive and therefore a higher velocity so the adjusted coefficients are nearly always non-zero. At the other extreme is flow from or into a near still body of water (e.g. a lake or the ocean). In this situation the loss coefficient(s) will not be reduced and the maximum energy loss possible should occur.

If the default adjust losses approach is used (<tt>Structure Losses</tt> <tt>==</tt><tt> ADJUST</tt>) the recommendations are to use industry guidelines for the entrance loss coefficient based on the shape/design of the inlet (these coefficients are typically based on a near zero approach velocity), and to use 1.0 for the exit loss coefficient. This applies to 1D culverts connected to 1D channels. The adjusted entrance and exit losses can be viewed over time in the _TSL layer, refer to the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW Manual]. 

Since the 2020-10-AA release, TUFLOW has a new (beta functionality) to have the losses automatically adjusted for linked 1D culverts and other structures connected to 2D domains through SX links (<tt>Structure Losses SX</tt> <tt>==</tt><tt> ADJUST</tt>), see Section 6.2 from the [https://downloads.tuflow.com/TUFLOW/Releases/2020-10/TUFLOW%20Release%20Notes.2020-10-AA.pdf 2020-10 Release Notes]. 

The other values to consider for modelling culverts are the inlet contraction coefficients used when the flow is upstream controlled flow should this occur. Typically the TUFLOW default values for these values should be used unless the inlet shape and design indicates otherwise. 

== Should I model pits as R or Q type? ==
Q pits are recommended for modelling kerb inlets, catchpits, drains, etc., provided that appropriate y-Q curves are available. TUFLOW automatically extends the y-Q curve for higher depths based on the orifice equation. Under downstream controlled flow regime it reverts to a drowned condition. 
When using the R pit, whilst the pit is free-falling into the pipe system below (most of the time until the system surcharges), entrance/exit losses don't apply, because the flow is inlet controlled. The R pit would be using the weir equation for unsubmerged flow or orifice flow for submerged flow and could be a poor representation of the pit flow behaviour, especially if the pit also has a grate and other non-rectangular characteristics.

== How to model pipe network with some pipe data missing? ==
In situations where the complete pipe network is not modelled there are numerous common approaches adopted by modellers:
* Use direct rainfall boundary conditions, model all inlets, only model the larger pipes in the pipe network. Where a small pipe is being omitted, connect the flows from the inlet to the main pipe network using virtual pipes functionality. Only the location of the inlet pits are needed, they can be approximated and sometimes lumped representation of the pits. This approach is especially good if the pipe network does not surcharge back to the overland flow anywhere or only in a minor way.
* Use lumped hydrology inflows. Only model the main pipe network and connected inlets (excluding the smaller features). Apply the flows directly to the pits either with:
** 1d_bc QT polygon boundaries with a P flag. This automatically distributes the total flow to all the pits within the polygon.
** 2d_sa boundaries with a "Read GIS SA PITS" command. The flow is applied to the ground surface rather than directly into the manhole. Some of the flow will surcharge out of the pits if the downstream pipe capacity is exceeded.

== How can I get more inflow into the pipe system? ==
A shallow depth can occur in the pit inlet cell and underestimate the depth at the entrance to the pit, restricting the amount of flow entering the pit. This can happen specifically for larger cell sizes. Different options to capture more water from 2D cells into the pits are available based on the TUFLOW engine. Both approaches aim to account for changes in ground elevation that occur at a finer scale than the 2D cell resolution:
*TUFLOW HPC - use Sub-Grid Sampling (SGS) - this approach samples underlying DEM with finer resolution and represents the true topography more accurately guiding water into the pits.
*TUFLOW Classic - use "Pit Default Road Crossfall" command to increase the depth at Q pits based on the crossfall slope of the road cross section. TUFLOW calculates the water depth that is used by the Pit Inlet Database using an adjacent side triangle depth approach, instead of the actual cell flow depth.

== How is flow through the culvert calculated? ==
Computationally, information is passed between the 1D and 2D on the half timestep:
* Flow through the culvert is calculated based on the water levels at the inlet/outlet and the assigned flow regime. The volume is extracted/added to the 2D boundary cells on the half timestep.
* On the next full timestep the 1D and 2D shallow water equations are applied to move water through their respective domains.

== What should I do if 1D pump doesn't convey as much flow as expected and/or seems to be unstable? ==
A couple of things can be checked:
* The inlet of the pipe has to be fully submerged, otherwise the pump will shut down and there will be no flow through the pump.
* Add extra storage to the node upstream of the pump with 1d_na table or 1d_nwk type NODE through ANA attribute.
* Try reasonably smaller 1D timestep.
* If using non-operational pump (P), check if the depth discharge relationship is appropriate. If outlet of the pipe is lower than inlet, the pump will always try to pump at full capacity.
* Consider using operational pump (PO), where the pump would switch off if the water level upstream gets below the pump soffit.

== How BB bridges automatically switch between pressure flow and being drowned out? ==
TUFLOW considers the top of the XZ or HW table as the soffit level. If the downstream water level exceeds the soffit level, TUFLOW does the pressure flow calculation using default K = 1.56. It compares the velocity calculated by the pressure flow equation and by the form loss calculation based on LC table and uses the smaller one.
The _TSF and _TSL layers can be used to find out the regime/form loss values used for BB bridges:
*"P": pressure flow
*"D": ds water level > the soffit level, but it applies normal flow because the normal flow equation predicts smaller velocity
*" ": ds water level < the soffit level, normal flow

== Should I model bridges in the 1D or 2D Domain? ==
The recommended approach typically depends on the study objectives and if the channel upstream and downstream of the bridge is modelled in 1D or 2D. To preserve the momentum as accurately as possible the bridge should be modelled in the same dimension as the channel, e.g. 1d_nwk bridge if the channels is in 1D and 2d_bg or 2d_lfcsh if the channel is modelled in 2D. 
In 2D, the expansion/contraction losses are modelled based on the topography and don't need to be estimated as attributes as for 1D modelling. Also, for higher flows where the bridge is overtopped, 2D is preferable approach.

== What is the difference between downstream and upstream controlled flow? ==
Downstream control means a change in downstream water level will cause a change in upstream water level. Upstream control means the upstream water level is insensitive to the downstream water level and usually indicates the occurrence of supercritical flow.

== How do negative "M", "N" and "R" values deactivate 1D cross-sections? ==
When negative “M” values are used to deactivate 1D cross-sections, it changes the flow area, wetted perimeter, hydraulic radius, and the conveyance, while keeping the storage width unchanged. It is like inserting a thin plate at the middle of the channel, as shown in the figure below. This approach is preferred to retain the storage in the system but reduce the conveyance (e.g., off-stream storage not contributing to conveyance or sections either side of a constriction to generate better approach/departure velocities if using the Total Area approach). 
'''Note:''' The negative "M" value must be a negative of a valid material ID. 
 
<ol>
[[File:Section deactivation M.png | 200px]] 
</ol>
When using negative (-1) “N” (Manning’s n) or “R” (relative resistance factor) to deactivate the 1D cross-sections, it reduces both the flow area and the storage width, as shown in the figure below. 
 
<ol>
[[File:Section deactivation N R.png | 200px]]
</ol>

==Calculating a 1d_bc head-discharge curve==

As of the 2023-03-AD release, it is possible for TUFLOW to automatically create a Head-Discharge curve for 1d_bc HQ boundaries. For more information see Section 3.5 of the [https://docs.tuflow.com/classic-hpc/release/2023-03-AE/OneDSolv-1.html#Auto1DHQ-2 2023-03 Release Notes].

If using older releases, this can be calculated manually as the condition to be satisfied for a normal depth is the Manning’s equation. The HQ depth should be set up with elevation and the discharge calculated from Manning’s equation (including Area and Hydraulic Radius for each elevation point) and can be calculated in a few steps: 

<ol>
<li>Set the model up your ESTRY model using a dummy downstream level boundary (e.g. a HQ boundary with no name, an error will be reported however the required check file will have been produced, otherwise any boundary will work) and run the model in test model (-t) so that check files are produced.
<li>From the check folder, open the '''*_1d_ta_tables.csv''' file.
<li>Find the entry for the most downstream cross-section (directly upstream of the 1d_bc HQ point). The '''*_1d_ta_tables.csv''' will contain a table of hydraulic parameters. The K entry highlighted is the cross-section conveyance in m3/s and is calculated as: 

K = 1/n * A * R2/3 

Where
* A is the Eff Area
* R is the Radius
 
[[File:Ta tables example.png | 800px ]] 

<li>Determine a representative slope (S), this could be taken from the last two cross-sections.
<li>Calculate the downstream Discharge (this is the same as calculated discharge from Mannings equation): 

Q = K * SQRT(S) 

Where:
* S is a representative slope value for the downstream reach.
* K is obtained from the '''1d_ta_tables.csv'''

<li>Use the Elevation (Head) vs Discharge pairs to populate the HQ table.
</ol>

==Why is the maximum 1D water level higher than 2D water level?==
TUFLOW calculates storage at 1D nodes (including manholes) and calculates flux at pipe mid-sections. The storages at the manholes are saved in the format of ‘elevation vs nodal area’ tables, which can be reviewed in the .eof file: 
<ol>
[[File:1DpitHmax 1.png]] 
</ol>

The change in water volume in a manhole is calculated based on the inflow from the pit above (if there is one) and the inflow/outflow from the connected pipes. The change in water level is then calculated based on the change in volume and the nodal area at that elevation. 
<ol>
[[File:1DpitHmax 2.png | 300px ]] 
</ol>

TUFLOW extends the ‘elevation vs nodal area’ table by 5 m (the last 2 numbers in the table above), which allows for the calculation of pressurised pipe flow when a manhole is drowned. If the maximum 1D water level is higher than the DEM, that means the manhole was drowned and pressurised during the peak of the flood. 
<ol>
[[File:1DpitHmax 3.png | 300px ]] 
</ol>

==How to represent the hydraulic behavior at the outlet of a pipe, including headwalls and transitions to open channels? ==
In general, pipe outlet headwalls affect the exit loss at the pipe outlet, and this can be modeled by changing the 1d_nwk “ExitC_or_WSb” attribute - exit loss coefficient for outlet controlled flow. The default exit loss coefficient is 1.0, assuming there is no headwall. For more details on these factors, refer to the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW Manual].
More guidance on applying entry/loss and contraction coefficients is available [[TUFLOW_1D_Channels_and_Hydraulic_Structures#What_entry.2Fexit_loss_and_contraction_coefficients_should_I_use_for_1D_culverts.3F | here]].

Once the water is in the 2D domain, headwalls can also affect the local flow field. The modelled hydraulic behavior depends on the model cell size. If the model has grid size smaller than the length of the headwall, thin breaklines or 2d_zsh tin can be added to represent the geometry in the 2D model. If the grid size is larger than the headwall, it only calculates ‘cell-averaged’ velocity including the impact of the headwall.

==Calculating velocity and bed shear stress at a pipe outlet==
The velocity and bed shear stress reported in the 2D domain at a pipe outlet depends on the model cell size. If the cell size is small enough and any outlets structures (i.e. wingwalls) are properly represented by the 2d cell/face elevations, TUFLOW can reasonably represent the flow field at the pipe outlet and estimate the bed shear stress base on the manning’s equation. However, be aware that TUFLOW solves 2D depth-averaged shallow water equation, so there is limitation of using it to estimate bed shear stress around complex structures or geometry that could experience vertical acceleration or re-circulation.

== How do I model blockage for irregular shaped culverts or channels? ==
For irregular shaped culverts and channels (1d_nwk "I" type), it is possible to model blockage by using the Skew (in degrees) attribute in the 1d_xs layer. This reduces the effective flow width of the cross section by rotating it relative to the direction of flow and can be used to approximate reduced capacity.

A skew angle of 0 means the cross section is aligned with the flow. As the angle increases, the effective width is reduced. For example, a skew of 60 degrees results results in a 50% reduction (as cos(60) = 0.5). While this does reduce the effective width of the structure, it isn’t expected to see the same reduction to conveyance as conveyance depends on the Manning’s n value, flow area and hydraulic radius.

== Do calculations differ if I specify multiple culverts using the Number_of attribute, even if the total dimensions are the same as a single culvert? ==
Yes. This can slightly affect the modelling results.

The key difference between a single wide culvert and multiple culverts with the same total width is the wetted perimeter. Multiple culverts have a slightly larger wetted perimeter due to the internal walls, which increases friction in downstream-controlled regimes. While friction losses are generally small compared to entry and exit losses, the impact on hydraulic behaviour is usually minor.

However, it’s important to model the configuration that best represents the actual structure when assessing culvert performance.

== How do I model a scruffy dome? ==
A scruffy dome is a type of dome inlet screen installed over a drop inlet, commonly used in stormwater systems. These inlets can be modelled in TUFLOW using a 1D Q type pit with a depth–discharge curve that reflects the hydraulic performance of the dome.

[https://www.ipwea-qnt.com/products-resources/qudm/ QUDM] recommends using the lesser of the weir flow or orifice flow equations to define inlet capacity (see Section 7.5.4). A blockage factor is also recommended to account for potential debris build-up during flood events (see Table 7.5.1 Provision for blockage at kerb inlets). This factor may vary depending on local guidelines, so regional standards should be consulted.

Once the rating curve is calculated, it can be added to the pit inlet database and linked to the Q type pit.
 
<ol>
[[File:How_do_I_model_a_scruffy_dome_in_TUFLOW.png|300x300px]] 
</ol>

== How can trash screens be represented? ==
A trash screen is a physical barrier, typically made from bars or mesh, placed at the inlet or outlet of a culvert, pipe, or channel to prevent debris from entering or exiting the structure.

Trash screens can be represented by applying additional head losses and blockage effects in accordance with [https://www.ipwea-qnt.com/products-resources/qudm/ QUDM] guidance. The approach depends on the location of the screen and the degree of blockage.

Possible use cases include:

* Where the screen is attached directly to the culvert inlet or outlet, the losses are applied to the culvert itself.
* Where the screen is located upstream of a culvert inlet or downstream of a culvert outlet, it can be modelled as a short zero length CF, IF, or RF channel before (upstream) or after (downstream) the culvert.
* In all cases, the net flow area should be reduced to account for the bars and any debris blockage, either by adjusting the flow area directly or by using the pBlockage attribute.

For example, the image below could be represented as a rectangular shaped screen located upstream or downstream of a culvert, modelled as a short zero length RF (rectangular culvert with fixed losses) channel using a 1d_nwk GIS layer.
<ol>
[[File:Trash_screen_schematic.png|alt=|300x300px]]
</ol>



== How can a flow splitter with a nib wall be modelled? ==
UNDER CONSTRUCTION

A flow splitter with a nib wall can be modelled by introducing a 1D weir between the manhole and the relevant outlet pipe. The weir crest elevation should be set equal to the height of the nib wall within the chamber. This setup allows low flows to discharge through one pipe, while higher flows overtop the weir into a secondary outlet, replicating the behaviour of a physical flow splitter.

The main outlet pipes are connected through a manhole, which automatically applies appropriate entry and exit losses. For outlets that do not connect further downstream, entry and exit losses should be manually defined in the attributes.

Refer to the schematic example below for this configuration.
<ol>
[[File:Manhole flow splitter topview.png|alt=|300x300px]]
</ol>
 

{{Tips Navigation
|uplink=[[ TUFLOW_Modelling_Guidance | Back to TUFLOW Modelling Guidance]]
}}

Hardware Benchmarking - Results

2026-01-19T23:24:58Z

Abrar.Alttahir: /* High End GPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"

! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||128||5200||32.7||108.6||141.3||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||64||5600||36.7||114.2||150.9||TM1
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||2.0||128||4000||41.2||124.1||165.3||AW1
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs)||3.2||128||4800||38.5||127.0||165.5||SB1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz||4.5||64||4800||33.4||136.0||169.4||PSM
|-
|12th Gen Intel(R) Core(TM) i9-12900K||3.2||128||3200||40.7||129.9||170.6||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz||2.1||64||4800||41||137.2||178.2||RB1
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs) @ ~4.2GHz||4.2||32||4800||35.5||162.8||198.3||LC1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs)||2.5||16||3200||48.4||170.5||218.9||TS1
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz||3.2||64||4800||53.3||169.1||222.4||CHR
|-
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|AMD Ryzen 5 7600X 6-Core Processor||4.7||32||4800||39.0||175.7||214.7||HP1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||3.6||64||2133||62.7||191.1||253.8||PA1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|3.2
|450
|
|50.0
|211.6
|261.6
|YW1
|-
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||3.2||56||N/A||49.1||218.9||268||RBR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||32||2113||68.2||217.9||286.1||HP2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||3.5||128||2666||66.3||234.7||301||JG3
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|2.6
|32
|3200
|66.6
|243.3
|309.9
|SK1
|-
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz||1.4||16||7467||173.8||152.8||326.6||AL1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||3.7||64||2933||95.4||231.3||326.7||JB2
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|AMD EPYC 7V12 64-Core Processor (16 CPUs) ||2.4||57||NA||63.5||277.6||341.1||CS2
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||3.8||64||3200||87.8||294.2||382||HDR-A2000
|-
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz ||3.0||4||986||84.0||351.4||435.4||TJS
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||3.5||16||1333||125.9||320.5||446.4||EFC
|-
|Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz||1.9||16||1800||100.1||347.0||447.1||SM1
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||4.1||16||4104||62.7||386.6||449.3||GZH
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||2.4||128||2394||130.5||331.5||462||VDI
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|-
|}

=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|2.5
|9.5
|12.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz ||NVIDIA GeForce RTX 4090||24||16384||2.4||11.6||14.0||JW1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4090||24||16384||2.5||12.1||14.6||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz ||NVIDIA GeForce RTX 4090||24||16384||2.7||12.7||15.4||TA1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||2.9||12.7||15.6||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||3.1||13.3||16.4||CR4
|-
|Intel(R) Core(TM) i9-14900K (32 CPUs) @ ~3.2GHz||NVIDIA GeForce RTX 4080 Super||16||10240||2.8||14.5||17.3||DF2
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||TM1
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||JM3
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||NVIDIA GeForce RTX 4080||16||9728||3.2||15.0||18.2||AW1
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|3.2
|15.0
|18.2
|TM2
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs)@ ~4.2GHz||NVIDIA GeForce RTX 4080 SUPER||16||10240||3.2||15.7||18.9||LC1
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||NVIDIA RTX 6000 Ada Generation||48||18176||4.9||14.2||19.1||JG3
|-
|Intel(R) Xeon(R) CPU E5-2697 v4 @ 2.30GHz (72 CPUs)||NVIDIA GeForce RTX 4090||24||16384||3.9||16.2||20.1||CS3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||3.4||19.2||22.6||HP1
|-
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|3.9
|19.07
|22.97
|PW1
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||3.8||19.2||23.0||RS1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||3.7||19.4||23.1||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700F
|NVIDIA GeForce RTX 3080 Ti
|12
|10240
|3.75
|19.57
|23.32
|AUBNE1PC6602
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||4.0||19.6||23.6||GP1
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA A40||48||10752||3.5||20.3||23.8||Lenovo SR650 V2
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.1||20.1||24.2||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz (36 CPUs), ~3.0GHz
|NVIDIA RTX A6000
|48
|10752
|4.7
|22.7
|27.4
|CB2
|-
|13th Gen Intel(R) Core(TM) i5-13600HX (20 CPUs) @ ~2.6GHz ||NVIDIA GeForce RTX 4090 Laptop GPU||16||9728||5.3||22.2||27.5||LC2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz ||NVIDIA GeForce RTX 3080||10||8704||4.3||23.7||28||RB1
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs) ||NVIDIA RTX A5000||24||8192||4.4||24.1||28.5||SB1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Xeon(R) W-2223 CPU @ 3.60GHz ||NVIDIA GeForce RTX 3090||24||10496||5.3||24.3||29.6||SSM2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen Threadripper 2920X 12-Core Processor (24 CPUs) @ 3.5GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.1||25.9||31||CR5
|-
|Intel(R) Xeon(R) W-2145 CPU @ 3.70GHz (16 CPUs), ~3.7GHz
|NVIDIA RTX A5000
|24
|8192
|5.2
|26.3
|31.5
|CB3
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.9||28.4||34.3||HP2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz ||NVIDIA GRID RTX8000P-48Q||48||4608||6.8||31.0||37.8||VJ1
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs) ||NVIDIA RTX A4000||16||6144||5.8||32.0||37.8||JS2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||6.0||32.0||38.0||EFC
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|7.2
|31.4
|38.6
|YW1
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs) @ 3.2GHz ||NVIDIA A10-24Q||24||8192||7.2||31.4||38.6||CS1
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||33.3||38.7||PA1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce RTX 3060 Ti (LHR) ||8||4864||5.7||33.3||39.0||DF1
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||NVIDIA GeForce RTX 2060 SUPER (core 1647MHz, mem 1750MHz)||8||2176||5.6||33.4||39.1||GZH
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||35.1||41.6||JB2
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz ||NVIDIA RTX A4000||16||6144||7.2||35.3||42.5||CHR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|AMD Ryzen 7 7840HS ||NVIDIA GeForce RTX 4060 Laptop GPU||8||3072||7.0||40.6||47.6||CH1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||NVIDIA GRID RTX6000P-4Q||24||4608||10.5||39.3||49.8||VDI
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA Tesla T4||16||2560||6.8||45.2||52.0||RJ1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz
|NVIDIA GeForce RTX 3060
|8
|3584
|7.68
|45.92
|53.6
|AUBNEW1DQ54G3
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||NVIDIA RTX A2000||6||3328||9.1||54.4||63.5||HDR-A2000
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz ||NVIDIA GeForce RTX 4050 Laptop GPU||6||2560||7.9||59.4||67.3||AL1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA GeForce GTX 1660||6||1408||9.4||61.0||70.4||JFP
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce GTX 1650 SUPER||4||1280||10.3||64.6||74.9||TS1
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|NVIDIA RTX A2000 Laptop GPU
|4
|2560
|11.4
|72.6
|84.0
|SK1
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz (64 CPUs), ~2.3GHz||NVIDIA Tesla M60||16||4096||12||82.7||94.7||SM2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i3-8100 CPU @ 3.60GHz ||NVIDIA Tesla K40c||12||2880||12.3||83.1||95.3||NWE
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA Quadro T2000||4||1024||13.3||85.2||95.5||SSM
|-
|Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz (Laptop) ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs), ~3.8GHz
|NVIDIA Quadro T1000
|4
|896
|13.6
|91.3
|104.9
|CB4
|-
|13th Gen Intel(R) Core(TM) i7-13800H (20 CPUs), ~2.5GHz
|NVIDIA RTX A500 Laptop GPU
|4
|2048
|14.8
|93.9
|108.7
|YB1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz||NVIDIA T500||4||986||29.3||197.5||226.8||TJS
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||NVIDIA A10-4Q||24||8192||44.5||212.6||257.1||RBR
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-

|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|73.3
|568.7
|642.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||NVIDIA GeForce RTX 4090||24||16384||103.3||811.2||914.5||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz||NVIDIA GeForce RTX 4090||24||16384||105.9||813.7||919.6||TA1
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz||NVIDIA GeForce RTX 4090||24||16384||103.7||824.3||928.0||JW1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||108.2||821.5||929.7||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||106.8||875.5||982.3||CR4
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz ||NVIDIA RTX 6000 Ada Generation||48||18176||119.6||877.8||997.4||JG3
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|134.0
|1049.9
|1183.9
|TM2
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||139.5||1115.1||1254.6||JM3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||155.5||1176.2||1331.7||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||159.7||1216.2||1375.9||GP1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||160.9||1240||1400.9||DD2
|-
|Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz ||NVIDIA GeForce RTX 3090||24||10496||172.9||1249.8||1422.7||SIP
|-
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|172.5
|1343.4
|15.15.9
|PW1
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||190.2||1493.2||1683.4||HP1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||215.9||1678.7||1894.6||PA2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||255.4||1691.7||1947.1||HP2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|242.3
|1856.5
|2098.8
|YW1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking | Back to TUFLOW Benchmarking]]
}}

Hardware Benchmarking - Results

2026-01-07T22:59:06Z

Abrar.Alttahir: /* CPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"

! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||128||5200||32.7||108.6||141.3||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||64||5600||36.7||114.2||150.9||TM1
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||2.0||128||4000||41.2||124.1||165.3||AW1
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs)||3.2||128||4800||38.5||127.0||165.5||SB1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz||4.5||64||4800||33.4||136.0||169.4||PSM
|-
|12th Gen Intel(R) Core(TM) i9-12900K||3.2||128||3200||40.7||129.9||170.6||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz||2.1||64||4800||41||137.2||178.2||RB1
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs) @ ~4.2GHz||4.2||32||4800||35.5||162.8||198.3||LC1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs)||2.5||16||3200||48.4||170.5||218.9||TS1
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz||3.2||64||4800||53.3||169.1||222.4||CHR
|-
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|AMD Ryzen 5 7600X 6-Core Processor||4.7||32||4800||39.0||175.7||214.7||HP1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||3.6||64||2133||62.7||191.1||253.8||PA1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|3.2
|450
|
|50.0
|211.6
|261.6
|YW1
|-
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||3.2||56||N/A||49.1||218.9||268||RBR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||32||2113||68.2||217.9||286.1||HP2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||3.5||128||2666||66.3||234.7||301||JG3
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|2.6
|32
|3200
|66.6
|243.3
|309.9
|SK1
|-
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz||1.4||16||7467||173.8||152.8||326.6||AL1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||3.7||64||2933||95.4||231.3||326.7||JB2
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|AMD EPYC 7V12 64-Core Processor (16 CPUs) ||2.4||57||NA||63.5||277.6||341.1||CS2
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||3.8||64||3200||87.8||294.2||382||HDR-A2000
|-
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz ||3.0||4||986||84.0||351.4||435.4||TJS
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||3.5||16||1333||125.9||320.5||446.4||EFC
|-
|Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz||1.9||16||1800||100.1||347.0||447.1||SM1
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||4.1||16||4104||62.7||386.6||449.3||GZH
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||2.4||128||2394||130.5||331.5||462||VDI
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|-
|}

=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|2.5
|9.5
|12.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz ||NVIDIA GeForce RTX 4090||24||16384||2.4||11.6||14.0||JW1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4090||24||16384||2.5||12.1||14.6||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz ||NVIDIA GeForce RTX 4090||24||16384||2.7||12.7||15.4||TA1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||2.9||12.7||15.6||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||3.1||13.3||16.4||CR4
|-
|Intel(R) Core(TM) i9-14900K (32 CPUs) @ ~3.2GHz||NVIDIA GeForce RTX 4080 Super||16||10240||2.8||14.5||17.3||DF2
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||TM1
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||JM3
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||NVIDIA GeForce RTX 4080||16||9728||3.2||15.0||18.2||AW1
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|3.2
|15.0
|18.2
|TM2
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs)@ ~4.2GHz||NVIDIA GeForce RTX 4080 SUPER||16||10240||3.2||15.7||18.9||LC1
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||NVIDIA RTX 6000 Ada Generation||48||18176||4.9||14.2||19.1||JG3
|-
|Intel(R) Xeon(R) CPU E5-2697 v4 @ 2.30GHz (72 CPUs)||NVIDIA GeForce RTX 4090||24||16384||3.9||16.2||20.1||CS3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||3.4||19.2||22.6||HP1
|-
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|3.9
|19.07
|22.97
|PW1
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||3.8||19.2||23.0||RS1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||3.7||19.4||23.1||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700F
|NVIDIA GeForce RTX 3080 Ti
|12
|10240
|3.75
|19.57
|23.32
|AUBNE1PC6602
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||4.0||19.6||23.6||GP1
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA A40||48||10752||3.5||20.3||23.8||Lenovo SR650 V2
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.1||20.1||24.2||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz (36 CPUs), ~3.0GHz
|NVIDIA RTX A6000
|48
|10752
|4.7
|22.7
|27.4
|CB2
|-
|13th Gen Intel(R) Core(TM) i5-13600HX (20 CPUs) @ ~2.6GHz ||NVIDIA GeForce RTX 4090 Laptop GPU||16||9728||5.3||22.2||27.5||LC2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz ||NVIDIA GeForce RTX 3080||10||8704||4.3||23.7||28||RB1
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs) ||NVIDIA RTX A5000||24||8192||4.4||24.1||28.5||SB1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Xeon(R) W-2223 CPU @ 3.60GHz ||NVIDIA GeForce RTX 3090||24||10496||5.3||24.3||29.6||SSM2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen Threadripper 2920X 12-Core Processor (24 CPUs) @ 3.5GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.1||25.9||31||CR5
|-
|Intel(R) Xeon(R) W-2145 CPU @ 3.70GHz (16 CPUs), ~3.7GHz
|NVIDIA RTX A5000
|24
|8192
|5.2
|26.3
|31.5
|CB3
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.9||28.4||34.3||HP2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz ||NVIDIA GRID RTX8000P-48Q||48||4608||6.8||31.0||37.8||VJ1
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs) ||NVIDIA RTX A4000||16||6144||5.8||32.0||37.8||JS2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||6.0||32.0||38.0||EFC
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|7.2
|31.4
|38.6
|YW1
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs) @ 3.2GHz ||NVIDIA A10-24Q||24||8192||7.2||31.4||38.6||CS1
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||33.3||38.7||PA1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce RTX 3060 Ti (LHR) ||8||4864||5.7||33.3||39.0||DF1
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||NVIDIA GeForce RTX 2060 SUPER (core 1647MHz, mem 1750MHz)||8||2176||5.6||33.4||39.1||GZH
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||35.1||41.6||JB2
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz ||NVIDIA RTX A4000||16||6144||7.2||35.3||42.5||CHR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|AMD Ryzen 7 7840HS ||NVIDIA GeForce RTX 4060 Laptop GPU||8||3072||7.0||40.6||47.6||CH1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||NVIDIA GRID RTX6000P-4Q||24||4608||10.5||39.3||49.8||VDI
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA Tesla T4||16||2560||6.8||45.2||52.0||RJ1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz
|NVIDIA GeForce RTX 3060
|8
|3584
|7.68
|45.92
|53.6
|AUBNEW1DQ54G3
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||NVIDIA RTX A2000||6||3328||9.1||54.4||63.5||HDR-A2000
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz ||NVIDIA GeForce RTX 4050 Laptop GPU||6||2560||7.9||59.4||67.3||AL1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA GeForce GTX 1660||6||1408||9.4||61.0||70.4||JFP
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce GTX 1650 SUPER||4||1280||10.3||64.6||74.9||TS1
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|NVIDIA RTX A2000 Laptop GPU
|4
|2560
|11.4
|72.6
|84.0
|SK1
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz (64 CPUs), ~2.3GHz||NVIDIA Tesla M60||16||4096||12||82.7||94.7||SM2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i3-8100 CPU @ 3.60GHz ||NVIDIA Tesla K40c||12||2880||12.3||83.1||95.3||NWE
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA Quadro T2000||4||1024||13.3||85.2||95.5||SSM
|-
|Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz (Laptop) ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs), ~3.8GHz
|NVIDIA Quadro T1000
|4
|896
|13.6
|91.3
|104.9
|CB4
|-
|13th Gen Intel(R) Core(TM) i7-13800H (20 CPUs), ~2.5GHz
|NVIDIA RTX A500 Laptop GPU
|4
|2048
|14.8
|93.9
|108.7
|YB1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz||NVIDIA T500||4||986||29.3||197.5||226.8||TJS
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||NVIDIA A10-4Q||24||8192||44.5||212.6||257.1||RBR
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-

|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|73.3
|568.7
|642.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||NVIDIA GeForce RTX 4090||24||16384||103.3||811.2||914.5||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz||NVIDIA GeForce RTX 4090||24||16384||105.9||813.7||919.6||TA1
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz||NVIDIA GeForce RTX 4090||24||16384||103.7||824.3||928.0||JW1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||108.2||821.5||929.7||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||106.8||875.5||982.3||CR4
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz ||NVIDIA RTX 6000 Ada Generation||48||18176||119.6||877.8||997.4||JG3
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|134.0
|1049.9
|1183.9
|TM2
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||139.5||1115.1||1254.6||JM3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||155.5||1176.2||1331.7||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||159.7||1216.2||1375.9||GP1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||160.9||1240||1400.9||DD2
|-
|Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz ||NVIDIA GeForce RTX 3090||24||10496||172.9||1249.8||1422.7||SIP
|-
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|172.5
|1343.4
|15.15.9
|PW1
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 2080 Ti||12||7680||190.2||1493.2||1683.4||HP1
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||215.9||1678.7||1894.6||PA2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||255.4||1691.7||1947.1||HP2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|242.3
|1856.5
|2098.8
|YW1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking | Back to TUFLOW Benchmarking]]
}}

Hardware Benchmarking - Results

2026-01-07T22:57:49Z

Abrar.Alttahir: /* High End GPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable"

! style="background-color:#005581; font-weight:bold; color:white;"| Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||128||5200||32.7||108.6||141.3||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||64||5600||36.7||114.2||150.9||TM1
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||2.0||128||4000||41.2||124.1||165.3||AW1
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs)||3.2||128||4800||38.5||127.0||165.5||SB1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz||4.5||64||4800||33.4||136.0||169.4||PSM
|-
|12th Gen Intel(R) Core(TM) i9-12900K||3.2||128||3200||40.7||129.9||170.6||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz||2.1||64||4800||41||137.2||178.2||RB1
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs) @ ~4.2GHz||4.2||32||4800||35.5||162.8||198.3||LC1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs)||2.5||16||3200||48.4||170.5||218.9||TS1
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz||3.2||64||4800||53.3||169.1||222.4||CHR
|-
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|AMD Ryzen 5 7600X 6-Core Processor||4.7||32||4800||39.0||175.7||214.7||HP1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||3.6||64||2133||62.7||191.1||253.8||PA1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|3.2
|450
|
|50.0
|211.6
|261.6
|YW1
|-
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||3.2||56||N/A||49.1||218.9||268||RBR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||32||2113||68.2||217.9||286.1||HP2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||3.5||128||2666||66.3||234.7||301||JG3
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|2.6
|32
|3200
|66.6
|243.3
|309.9
|SK1
|-
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz||1.4||16||7467||173.8||152.8||326.6||AL1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||3.7||64||2933||95.4||231.3||326.7||JB2
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|AMD EPYC 7V12 64-Core Processor (16 CPUs) ||2.4||57||NA||63.5||277.6||341.1||CS2
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||3.8||64||3200||87.8||294.2||382||HDR-A2000
|-
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz ||3.0||4||986||84.0||351.4||435.4||TJS
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||3.5||16||1333||125.9||320.5||446.4||EFC
|-
|Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz||1.9||16||1800||100.1||347.0||447.1||SM1
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||4.1||16||4104||62.7||386.6||449.3||GZH
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||2.4||128||2394||130.5||331.5||462||VDI
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|-
|}
=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|2.5
|9.5
|12.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz ||NVIDIA GeForce RTX 4090||24||16384||2.4||11.6||14.0||JW1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4090||24||16384||2.5||12.1||14.6||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz ||NVIDIA GeForce RTX 4090||24||16384||2.7||12.7||15.4||TA1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||2.9||12.7||15.6||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||3.1||13.3||16.4||CR4
|-
|Intel(R) Core(TM) i9-14900K (32 CPUs) @ ~3.2GHz||NVIDIA GeForce RTX 4080 Super||16||10240||2.8||14.5||17.3||DF2
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||TM1
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||JM3
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||NVIDIA GeForce RTX 4080||16||9728||3.2||15.0||18.2||AW1
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|3.2
|15.0
|18.2
|TM2
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs)@ ~4.2GHz||NVIDIA GeForce RTX 4080 SUPER||16||10240||3.2||15.7||18.9||LC1
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||NVIDIA RTX 6000 Ada Generation||48||18176||4.9||14.2||19.1||JG3
|-
|Intel(R) Xeon(R) CPU E5-2697 v4 @ 2.30GHz (72 CPUs)||NVIDIA GeForce RTX 4090||24||16384||3.9||16.2||20.1||CS3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||3.4||19.2||22.6||HP1
|-
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|3.9
|19.07
|22.97
|PW1
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||3.8||19.2||23.0||RS1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||3.7||19.4||23.1||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700F
|NVIDIA GeForce RTX 3080 Ti
|12
|10240
|3.75
|19.57
|23.32
|AUBNE1PC6602
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||4.0||19.6||23.6||GP1
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA A40||48||10752||3.5||20.3||23.8||Lenovo SR650 V2
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.1||20.1||24.2||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz (36 CPUs), ~3.0GHz
|NVIDIA RTX A6000
|48
|10752
|4.7
|22.7
|27.4
|CB2
|-
|13th Gen Intel(R) Core(TM) i5-13600HX (20 CPUs) @ ~2.6GHz ||NVIDIA GeForce RTX 4090 Laptop GPU||16||9728||5.3||22.2||27.5||LC2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz ||NVIDIA GeForce RTX 3080||10||8704||4.3||23.7||28||RB1
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs) ||NVIDIA RTX A5000||24||8192||4.4||24.1||28.5||SB1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Xeon(R) W-2223 CPU @ 3.60GHz ||NVIDIA GeForce RTX 3090||24||10496||5.3||24.3||29.6||SSM2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen Threadripper 2920X 12-Core Processor (24 CPUs) @ 3.5GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.1||25.9||31||CR5
|-
|Intel(R) Xeon(R) W-2145 CPU @ 3.70GHz (16 CPUs), ~3.7GHz
|NVIDIA RTX A5000
|24
|8192
|5.2
|26.3
|31.5
|CB3
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.9||28.4||34.3||HP2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz ||NVIDIA GRID RTX8000P-48Q||48||4608||6.8||31.0||37.8||VJ1
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs) ||NVIDIA RTX A4000||16||6144||5.8||32.0||37.8||JS2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||6.0||32.0||38.0||EFC
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|7.2
|31.4
|38.6
|YW1
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs) @ 3.2GHz ||NVIDIA A10-24Q||24||8192||7.2||31.4||38.6||CS1
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||33.3||38.7||PA1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce RTX 3060 Ti (LHR) ||8||4864||5.7||33.3||39.0||DF1
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||NVIDIA GeForce RTX 2060 SUPER (core 1647MHz, mem 1750MHz)||8||2176||5.6||33.4||39.1||GZH
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||35.1||41.6||JB2
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz ||NVIDIA RTX A4000||16||6144||7.2||35.3||42.5||CHR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|AMD Ryzen 7 7840HS ||NVIDIA GeForce RTX 4060 Laptop GPU||8||3072||7.0||40.6||47.6||CH1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||NVIDIA GRID RTX6000P-4Q||24||4608||10.5||39.3||49.8||VDI
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA Tesla T4||16||2560||6.8||45.2||52.0||RJ1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz
|NVIDIA GeForce RTX 3060
|8
|3584
|7.68
|45.92
|53.6
|AUBNEW1DQ54G3
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||NVIDIA RTX A2000||6||3328||9.1||54.4||63.5||HDR-A2000
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz ||NVIDIA GeForce RTX 4050 Laptop GPU||6||2560||7.9||59.4||67.3||AL1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA GeForce GTX 1660||6||1408||9.4||61.0||70.4||JFP
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce GTX 1650 SUPER||4||1280||10.3||64.6||74.9||TS1
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|NVIDIA RTX A2000 Laptop GPU
|4
|2560
|11.4
|72.6
|84.0
|SK1
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz (64 CPUs), ~2.3GHz||NVIDIA Tesla M60||16||4096||12||82.7||94.7||SM2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i3-8100 CPU @ 3.60GHz ||NVIDIA Tesla K40c||12||2880||12.3||83.1||95.3||NWE
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA Quadro T2000||4||1024||13.3||85.2||95.5||SSM
|-
|Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz (Laptop) ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs), ~3.8GHz
|NVIDIA Quadro T1000
|4
|896
|13.6
|91.3
|104.9
|CB4
|-
|13th Gen Intel(R) Core(TM) i7-13800H (20 CPUs), ~2.5GHz
|NVIDIA RTX A500 Laptop GPU
|4
|2048
|14.8
|93.9
|108.7
|YB1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz||NVIDIA T500||4||986||29.3||197.5||226.8||TJS
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||NVIDIA A10-4Q||24||8192||44.5||212.6||257.1||RBR
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-

|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|73.3
|568.7
|642.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||NVIDIA GeForce RTX 4090||24||16384||103.3||811.2||914.5||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz||NVIDIA GeForce RTX 4090||24||16384||105.9||813.7||919.6||TA1
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz||NVIDIA GeForce RTX 4090||24||16384||103.7||824.3||928.0||JW1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||108.2||821.5||929.7||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||106.8||875.5||982.3||CR4
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz ||NVIDIA RTX 6000 Ada Generation||48||18176||119.6||877.8||997.4||JG3
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|134.0
|1049.9
|1183.9
|TM2
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||139.5||1115.1||1254.6||JM3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||155.5||1176.2||1331.7||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||159.7||1216.2||1375.9||GP1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||160.9||1240||1400.9||DD2
|-
|Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz ||NVIDIA GeForce RTX 3090||24||10496||172.9||1249.8||1422.7||SIP
|-
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|172.5
|1343.4
|15.15.9
|PW1
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 2080 Ti||12||7680||190.2||1493.2||1683.4||HP1
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||215.9||1678.7||1894.6||PA2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||255.4||1691.7||1947.1||HP2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|242.3
|1856.5
|2098.8
|YW1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking | Back to TUFLOW Benchmarking]]
}}

Hardware Benchmarking - Results

2026-01-07T22:56:41Z

Abrar.Alttahir: /* GPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable"

! style="background-color:#005581; font-weight:bold; color:white;"| Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||128||5200||32.7||108.6||141.3||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||64||5600||36.7||114.2||150.9||TM1
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||2.0||128||4000||41.2||124.1||165.3||AW1
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs)||3.2||128||4800||38.5||127.0||165.5||SB1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz||4.5||64||4800||33.4||136.0||169.4||PSM
|-
|12th Gen Intel(R) Core(TM) i9-12900K||3.2||128||3200||40.7||129.9||170.6||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz||2.1||64||4800||41||137.2||178.2||RB1
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs) @ ~4.2GHz||4.2||32||4800||35.5||162.8||198.3||LC1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs)||2.5||16||3200||48.4||170.5||218.9||TS1
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz||3.2||64||4800||53.3||169.1||222.4||CHR
|-
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|AMD Ryzen 5 7600X 6-Core Processor||4.7||32||4800||39.0||175.7||214.7||HP1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||3.6||64||2133||62.7||191.1||253.8||PA1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|3.2
|450
|
|50.0
|211.6
|261.6
|YW1
|-
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||3.2||56||N/A||49.1||218.9||268||RBR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||32||2113||68.2||217.9||286.1||HP2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||3.5||128||2666||66.3||234.7||301||JG3
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|2.6
|32
|3200
|66.6
|243.3
|309.9
|SK1
|-
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz||1.4||16||7467||173.8||152.8||326.6||AL1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||3.7||64||2933||95.4||231.3||326.7||JB2
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|AMD EPYC 7V12 64-Core Processor (16 CPUs) ||2.4||57||NA||63.5||277.6||341.1||CS2
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||3.8||64||3200||87.8||294.2||382||HDR-A2000
|-
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz ||3.0||4||986||84.0||351.4||435.4||TJS
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||3.5||16||1333||125.9||320.5||446.4||EFC
|-
|Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz||1.9||16||1800||100.1||347.0||447.1||SM1
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||4.1||16||4104||62.7||386.6||449.3||GZH
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||2.4||128||2394||130.5||331.5||462||VDI
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|-
|}
=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable" style="position:relative;"
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white; position:sticky; top:0; z-index:2;" width=12% | System Name
|-
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|2.5
|9.5
|12.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz ||NVIDIA GeForce RTX 4090||24||16384||2.4||11.6||14.0||JW1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4090||24||16384||2.5||12.1||14.6||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz ||NVIDIA GeForce RTX 4090||24||16384||2.7||12.7||15.4||TA1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||2.9||12.7||15.6||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||3.1||13.3||16.4||CR4
|-
|Intel(R) Core(TM) i9-14900K (32 CPUs) @ ~3.2GHz||NVIDIA GeForce RTX 4080 Super||16||10240||2.8||14.5||17.3||DF2
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||TM1
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||JM3
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||NVIDIA GeForce RTX 4080||16||9728||3.2||15.0||18.2||AW1
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|3.2
|15.0
|18.2
|TM2
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs)@ ~4.2GHz||NVIDIA GeForce RTX 4080 SUPER||16||10240||3.2||15.7||18.9||LC1
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||NVIDIA RTX 6000 Ada Generation||48||18176||4.9||14.2||19.1||JG3
|-
|Intel(R) Xeon(R) CPU E5-2697 v4 @ 2.30GHz (72 CPUs)||NVIDIA GeForce RTX 4090||24||16384||3.9||16.2||20.1||CS3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||3.4||19.2||22.6||HP1
|-
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|3.9
|19.07
|22.97
|PW1
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||3.8||19.2||23.0||RS1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||3.7||19.4||23.1||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700F
|NVIDIA GeForce RTX 3080 Ti
|12
|10240
|3.75
|19.57
|23.32
|AUBNE1PC6602
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||4.0||19.6||23.6||GP1
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA A40||48||10752||3.5||20.3||23.8||Lenovo SR650 V2
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.1||20.1||24.2||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz (36 CPUs), ~3.0GHz
|NVIDIA RTX A6000
|48
|10752
|4.7
|22.7
|27.4
|CB2
|-
|13th Gen Intel(R) Core(TM) i5-13600HX (20 CPUs) @ ~2.6GHz ||NVIDIA GeForce RTX 4090 Laptop GPU||16||9728||5.3||22.2||27.5||LC2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz ||NVIDIA GeForce RTX 3080||10||8704||4.3||23.7||28||RB1
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs) ||NVIDIA RTX A5000||24||8192||4.4||24.1||28.5||SB1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Xeon(R) W-2223 CPU @ 3.60GHz ||NVIDIA GeForce RTX 3090||24||10496||5.3||24.3||29.6||SSM2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen Threadripper 2920X 12-Core Processor (24 CPUs) @ 3.5GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.1||25.9||31||CR5
|-
|Intel(R) Xeon(R) W-2145 CPU @ 3.70GHz (16 CPUs), ~3.7GHz
|NVIDIA RTX A5000
|24
|8192
|5.2
|26.3
|31.5
|CB3
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.9||28.4||34.3||HP2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz ||NVIDIA GRID RTX8000P-48Q||48||4608||6.8||31.0||37.8||VJ1
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs) ||NVIDIA RTX A4000||16||6144||5.8||32.0||37.8||JS2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||6.0||32.0||38.0||EFC
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|7.2
|31.4
|38.6
|YW1
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs) @ 3.2GHz ||NVIDIA A10-24Q||24||8192||7.2||31.4||38.6||CS1
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||33.3||38.7||PA1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce RTX 3060 Ti (LHR) ||8||4864||5.7||33.3||39.0||DF1
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||NVIDIA GeForce RTX 2060 SUPER (core 1647MHz, mem 1750MHz)||8||2176||5.6||33.4||39.1||GZH
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||35.1||41.6||JB2
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz ||NVIDIA RTX A4000||16||6144||7.2||35.3||42.5||CHR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|AMD Ryzen 7 7840HS ||NVIDIA GeForce RTX 4060 Laptop GPU||8||3072||7.0||40.6||47.6||CH1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||NVIDIA GRID RTX6000P-4Q||24||4608||10.5||39.3||49.8||VDI
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA Tesla T4||16||2560||6.8||45.2||52.0||RJ1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz
|NVIDIA GeForce RTX 3060
|8
|3584
|7.68
|45.92
|53.6
|AUBNEW1DQ54G3
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||NVIDIA RTX A2000||6||3328||9.1||54.4||63.5||HDR-A2000
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz ||NVIDIA GeForce RTX 4050 Laptop GPU||6||2560||7.9||59.4||67.3||AL1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA GeForce GTX 1660||6||1408||9.4||61.0||70.4||JFP
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce GTX 1650 SUPER||4||1280||10.3||64.6||74.9||TS1
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|NVIDIA RTX A2000 Laptop GPU
|4
|2560
|11.4
|72.6
|84.0
|SK1
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz (64 CPUs), ~2.3GHz||NVIDIA Tesla M60||16||4096||12||82.7||94.7||SM2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i3-8100 CPU @ 3.60GHz ||NVIDIA Tesla K40c||12||2880||12.3||83.1||95.3||NWE
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA Quadro T2000||4||1024||13.3||85.2||95.5||SSM
|-
|Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz (Laptop) ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs), ~3.8GHz
|NVIDIA Quadro T1000
|4
|896
|13.6
|91.3
|104.9
|CB4
|-
|13th Gen Intel(R) Core(TM) i7-13800H (20 CPUs), ~2.5GHz
|NVIDIA RTX A500 Laptop GPU
|4
|2048
|14.8
|93.9
|108.7
|YB1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz||NVIDIA T500||4||986||29.3||197.5||226.8||TJS
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||NVIDIA A10-4Q||24||8192||44.5||212.6||257.1||RBR
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|73.3
|568.7
|642.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||NVIDIA GeForce RTX 4090||24||16384||103.3||811.2||914.5||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz||NVIDIA GeForce RTX 4090||24||16384||105.9||813.7||919.6||TA1
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz||NVIDIA GeForce RTX 4090||24||16384||103.7||824.3||928.0||JW1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||108.2||821.5||929.7||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||106.8||875.5||982.3||CR4
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz ||NVIDIA RTX 6000 Ada Generation||48||18176||119.6||877.8||997.4||JG3
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|134.0
|1049.9
|1183.9
|TM2
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||139.5||1115.1||1254.6||JM3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||155.5||1176.2||1331.7||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||159.7||1216.2||1375.9||GP1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||160.9||1240||1400.9||DD2
|-
|Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz ||NVIDIA GeForce RTX 3090||24||10496||172.9||1249.8||1422.7||SIP
|-
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|172.5
|1343.4
|15.15.9
|PW1
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 2080 Ti||12||7680||190.2||1493.2||1683.4||HP1
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||215.9||1678.7||1894.6||PA2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||255.4||1691.7||1947.1||HP2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|242.3
|1856.5
|2098.8
|YW1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking | Back to TUFLOW Benchmarking]]
}}

Hardware Benchmarking - Results

2026-01-07T03:58:09Z

Abrar.Alttahir: /* High End GPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable"

! style="background-color:#005581; font-weight:bold; color:white;"| Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||128||5200||32.7||108.6||141.3||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||64||5600||36.7||114.2||150.9||TM1
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||2.0||128||4000||41.2||124.1||165.3||AW1
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs)||3.2||128||4800||38.5||127.0||165.5||SB1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz||4.5||64||4800||33.4||136.0||169.4||PSM
|-
|12th Gen Intel(R) Core(TM) i9-12900K||3.2||128||3200||40.7||129.9||170.6||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz||2.1||64||4800||41||137.2||178.2||RB1
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs) @ ~4.2GHz||4.2||32||4800||35.5||162.8||198.3||LC1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs)||2.5||16||3200||48.4||170.5||218.9||TS1
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz||3.2||64||4800||53.3||169.1||222.4||CHR
|-
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|AMD Ryzen 5 7600X 6-Core Processor||4.7||32||4800||39.0||175.7||214.7||HP1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||3.6||64||2133||62.7||191.1||253.8||PA1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|3.2
|450
|
|50.0
|211.6
|261.6
|YW1
|-
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||3.2||56||N/A||49.1||218.9||268||RBR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||32||2113||68.2||217.9||286.1||HP2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||3.5||128||2666||66.3||234.7||301||JG3
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|2.6
|32
|3200
|66.6
|243.3
|309.9
|SK1
|-
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz||1.4||16||7467||173.8||152.8||326.6||AL1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||3.7||64||2933||95.4||231.3||326.7||JB2
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|AMD EPYC 7V12 64-Core Processor (16 CPUs) ||2.4||57||NA||63.5||277.6||341.1||CS2
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||3.8||64||3200||87.8||294.2||382||HDR-A2000
|-
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz ||3.0||4||986||84.0||351.4||435.4||TJS
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||3.5||16||1333||125.9||320.5||446.4||EFC
|-
|Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz||1.9||16||1800||100.1||347.0||447.1||SM1
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||4.1||16||4104||62.7||386.6||449.3||GZH
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||2.4||128||2394||130.5||331.5||462||VDI
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|-
|}
=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|2.5
|9.5
|12.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz ||NVIDIA GeForce RTX 4090||24||16384||2.4||11.6||14.0||JW1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4090||24||16384||2.5||12.1||14.6||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz ||NVIDIA GeForce RTX 4090||24||16384||2.7||12.7||15.4||TA1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||2.9||12.7||15.6||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||3.1||13.3||16.4||CR4
|-
|Intel(R) Core(TM) i9-14900K (32 CPUs) @ ~3.2GHz||NVIDIA GeForce RTX 4080 Super||16||10240||2.8||14.5||17.3||DF2
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||TM1
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||JM3
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||NVIDIA GeForce RTX 4080||16||9728||3.2||15.0||18.2||AW1
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|3.2
|15.0
|18.2
|TM2
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs)@ ~4.2GHz||NVIDIA GeForce RTX 4080 SUPER||16||10240||3.2||15.7||18.9||LC1
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||NVIDIA RTX 6000 Ada Generation||48||18176||4.9||14.2||19.1||JG3
|-
|Intel(R) Xeon(R) CPU E5-2697 v4 @ 2.30GHz (72 CPUs)||NVIDIA GeForce RTX 4090||24||16384||3.9||16.2||20.1||CS3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||3.4||19.2||22.6||HP1
|-
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|3.9
|19.07
|22.97
|PW1
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||3.8||19.2||23.0||RS1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||3.7||19.4||23.1||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700F
|NVIDIA GeForce RTX 3080 Ti
|12
|10240
|3.75
|19.57
|23.32
|AUBNE1PC6602
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||4.0||19.6||23.6||GP1
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA A40||48||10752||3.5||20.3||23.8||Lenovo SR650 V2
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.1||20.1||24.2||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz (36 CPUs), ~3.0GHz
|NVIDIA RTX A6000
|48
|10752
|4.7
|22.7
|27.4
|CB2
|-
|13th Gen Intel(R) Core(TM) i5-13600HX (20 CPUs) @ ~2.6GHz ||NVIDIA GeForce RTX 4090 Laptop GPU||16||9728||5.3||22.2||27.5||LC2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz ||NVIDIA GeForce RTX 3080||10||8704||4.3||23.7||28||RB1
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs) ||NVIDIA RTX A5000||24||8192||4.4||24.1||28.5||SB1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Xeon(R) W-2223 CPU @ 3.60GHz ||NVIDIA GeForce RTX 3090||24||10496||5.3||24.3||29.6||SSM2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen Threadripper 2920X 12-Core Processor (24 CPUs) @ 3.5GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.1||25.9||31||CR5
|-
|Intel(R) Xeon(R) W-2145 CPU @ 3.70GHz (16 CPUs), ~3.7GHz
|NVIDIA RTX A5000
|24
|8192
|5.2
|26.3
|31.5
|CB3
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.9||28.4||34.3||HP2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz ||NVIDIA GRID RTX8000P-48Q||48||4608||6.8||31.0||37.8||VJ1
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs) ||NVIDIA RTX A4000||16||6144||5.8||32.0||37.8||JS2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||6.0||32.0||38.0||EFC
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|7.2
|31.4
|38.6
|YW1
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs) @ 3.2GHz ||NVIDIA A10-24Q||24||8192||7.2||31.4||38.6||CS1
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||33.3||38.7||PA1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce RTX 3060 Ti (LHR) ||8||4864||5.7||33.3||39.0||DF1
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||NVIDIA GeForce RTX 2060 SUPER (core 1647MHz, mem 1750MHz)||8||2176||5.6||33.4||39.1||GZH
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||35.1||41.6||JB2
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz ||NVIDIA RTX A4000||16||6144||7.2||35.3||42.5||CHR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|AMD Ryzen 7 7840HS ||NVIDIA GeForce RTX 4060 Laptop GPU||8||3072||7.0||40.6||47.6||CH1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||NVIDIA GRID RTX6000P-4Q||24||4608||10.5||39.3||49.8||VDI
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA Tesla T4||16||2560||6.8||45.2||52.0||RJ1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz
|NVIDIA GeForce RTX 3060
|8
|3584
|7.68
|45.92
|53.6
|AUBNEW1DQ54G3
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||NVIDIA RTX A2000||6||3328||9.1||54.4||63.5||HDR-A2000
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz ||NVIDIA GeForce RTX 4050 Laptop GPU||6||2560||7.9||59.4||67.3||AL1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA GeForce GTX 1660||6||1408||9.4||61.0||70.4||JFP
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce GTX 1650 SUPER||4||1280||10.3||64.6||74.9||TS1
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|NVIDIA RTX A2000 Laptop GPU
|4
|2560
|11.4
|72.6
|84.0
|SK1
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz (64 CPUs), ~2.3GHz||NVIDIA Tesla M60||16||4096||12||82.7||94.7||SM2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i3-8100 CPU @ 3.60GHz ||NVIDIA Tesla K40c||12||2880||12.3||83.1||95.3||NWE
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA Quadro T2000||4||1024||13.3||85.2||95.5||SSM
|-
|Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz (Laptop) ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs), ~3.8GHz
|NVIDIA Quadro T1000
|4
|896
|13.6
|91.3
|104.9
|CB4
|-
|13th Gen Intel(R) Core(TM) i7-13800H (20 CPUs), ~2.5GHz
|NVIDIA RTX A500 Laptop GPU
|4
|2048
|14.8
|93.9
|108.7
|YB1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz||NVIDIA T500||4||986||29.3||197.5||226.8||TJS
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||NVIDIA A10-4Q||24||8192||44.5||212.6||257.1||RBR
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|73.3
|568.7
|642.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||NVIDIA GeForce RTX 4090||24||16384||103.3||811.2||914.5||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz||NVIDIA GeForce RTX 4090||24||16384||105.9||813.7||919.6||TA1
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz||NVIDIA GeForce RTX 4090||24||16384||103.7||824.3||928.0||JW1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||108.2||821.5||929.7||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||106.8||875.5||982.3||CR4
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz ||NVIDIA RTX 6000 Ada Generation||48||18176||119.6||877.8||997.4||JG3
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|134.0
|1049.9
|1183.9
|TM2
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||139.5||1115.1||1254.6||JM3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||155.5||1176.2||1331.7||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||159.7||1216.2||1375.9||GP1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||160.9||1240||1400.9||DD2
|-
|Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz ||NVIDIA GeForce RTX 3090||24||10496||172.9||1249.8||1422.7||SIP
|-
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|172.5
|1343.4
|15.15.9
|PW1
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 2080 Ti||12||7680||190.2||1493.2||1683.4||HP1
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||215.9||1678.7||1894.6||PA2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||255.4||1691.7||1947.1||HP2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|242.3
|1856.5
|2098.8
|YW1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking | Back to TUFLOW Benchmarking]]
}}

Hardware Benchmarking - Results

2026-01-07T03:49:58Z

Abrar.Alttahir: /* GPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable"

! style="background-color:#005581; font-weight:bold; color:white;"| Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||128||5200||32.7||108.6||141.3||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||64||5600||36.7||114.2||150.9||TM1
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||2.0||128||4000||41.2||124.1||165.3||AW1
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs)||3.2||128||4800||38.5||127.0||165.5||SB1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz||4.5||64||4800||33.4||136.0||169.4||PSM
|-
|12th Gen Intel(R) Core(TM) i9-12900K||3.2||128||3200||40.7||129.9||170.6||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz||2.1||64||4800||41||137.2||178.2||RB1
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs) @ ~4.2GHz||4.2||32||4800||35.5||162.8||198.3||LC1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs)||2.5||16||3200||48.4||170.5||218.9||TS1
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz||3.2||64||4800||53.3||169.1||222.4||CHR
|-
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|AMD Ryzen 5 7600X 6-Core Processor||4.7||32||4800||39.0||175.7||214.7||HP1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||3.6||64||2133||62.7||191.1||253.8||PA1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|3.2
|450
|
|50.0
|211.6
|261.6
|YW1
|-
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||3.2||56||N/A||49.1||218.9||268||RBR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||32||2113||68.2||217.9||286.1||HP2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||3.5||128||2666||66.3||234.7||301||JG3
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|2.6
|32
|3200
|66.6
|243.3
|309.9
|SK1
|-
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz||1.4||16||7467||173.8||152.8||326.6||AL1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||3.7||64||2933||95.4||231.3||326.7||JB2
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|AMD EPYC 7V12 64-Core Processor (16 CPUs) ||2.4||57||NA||63.5||277.6||341.1||CS2
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||3.8||64||3200||87.8||294.2||382||HDR-A2000
|-
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz ||3.0||4||986||84.0||351.4||435.4||TJS
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||3.5||16||1333||125.9||320.5||446.4||EFC
|-
|Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz||1.9||16||1800||100.1||347.0||447.1||SM1
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||4.1||16||4104||62.7||386.6||449.3||GZH
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||2.4||128||2394||130.5||331.5||462||VDI
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|-
|}
=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|2.5
|9.5
|12.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz ||NVIDIA GeForce RTX 4090||24||16384||2.4||11.6||14.0||JW1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4090||24||16384||2.5||12.1||14.6||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz ||NVIDIA GeForce RTX 4090||24||16384||2.7||12.7||15.4||TA1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||2.9||12.7||15.6||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||3.1||13.3||16.4||CR4
|-
|Intel(R) Core(TM) i9-14900K (32 CPUs) @ ~3.2GHz||NVIDIA GeForce RTX 4080 Super||16||10240||2.8||14.5||17.3||DF2
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||TM1
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||JM3
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||NVIDIA GeForce RTX 4080||16||9728||3.2||15.0||18.2||AW1
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|3.2
|15.0
|18.2
|TM2
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs)@ ~4.2GHz||NVIDIA GeForce RTX 4080 SUPER||16||10240||3.2||15.7||18.9||LC1
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||NVIDIA RTX 6000 Ada Generation||48||18176||4.9||14.2||19.1||JG3
|-
|Intel(R) Xeon(R) CPU E5-2697 v4 @ 2.30GHz (72 CPUs)||NVIDIA GeForce RTX 4090||24||16384||3.9||16.2||20.1||CS3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||3.4||19.2||22.6||HP1
|-
|AMD Ryzen 7 5800X 8-Core Processor (~3.8 GHz)
|NVIDIA GeForce RTX 5070
|12
|6144
|3.9
|19.07
|22.97
|PW1
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||3.8||19.2||23.0||RS1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||3.7||19.4||23.1||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700F
|NVIDIA GeForce RTX 3080 Ti
|12
|10240
|3.75
|19.57
|23.32
|AUBNE1PC6602
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||4.0||19.6||23.6||GP1
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA A40||48||10752||3.5||20.3||23.8||Lenovo SR650 V2
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.1||20.1||24.2||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz (36 CPUs), ~3.0GHz
|NVIDIA RTX A6000
|48
|10752
|4.7
|22.7
|27.4
|CB2
|-
|13th Gen Intel(R) Core(TM) i5-13600HX (20 CPUs) @ ~2.6GHz ||NVIDIA GeForce RTX 4090 Laptop GPU||16||9728||5.3||22.2||27.5||LC2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz ||NVIDIA GeForce RTX 3080||10||8704||4.3||23.7||28||RB1
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs) ||NVIDIA RTX A5000||24||8192||4.4||24.1||28.5||SB1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Xeon(R) W-2223 CPU @ 3.60GHz ||NVIDIA GeForce RTX 3090||24||10496||5.3||24.3||29.6||SSM2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen Threadripper 2920X 12-Core Processor (24 CPUs) @ 3.5GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.1||25.9||31||CR5
|-
|Intel(R) Xeon(R) W-2145 CPU @ 3.70GHz (16 CPUs), ~3.7GHz
|NVIDIA RTX A5000
|24
|8192
|5.2
|26.3
|31.5
|CB3
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.9||28.4||34.3||HP2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz ||NVIDIA GRID RTX8000P-48Q||48||4608||6.8||31.0||37.8||VJ1
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs) ||NVIDIA RTX A4000||16||6144||5.8||32.0||37.8||JS2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||6.0||32.0||38.0||EFC
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|7.2
|31.4
|38.6
|YW1
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs) @ 3.2GHz ||NVIDIA A10-24Q||24||8192||7.2||31.4||38.6||CS1
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||33.3||38.7||PA1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce RTX 3060 Ti (LHR) ||8||4864||5.7||33.3||39.0||DF1
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||NVIDIA GeForce RTX 2060 SUPER (core 1647MHz, mem 1750MHz)||8||2176||5.6||33.4||39.1||GZH
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||35.1||41.6||JB2
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz ||NVIDIA RTX A4000||16||6144||7.2||35.3||42.5||CHR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|AMD Ryzen 7 7840HS ||NVIDIA GeForce RTX 4060 Laptop GPU||8||3072||7.0||40.6||47.6||CH1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||NVIDIA GRID RTX6000P-4Q||24||4608||10.5||39.3||49.8||VDI
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA Tesla T4||16||2560||6.8||45.2||52.0||RJ1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz
|NVIDIA GeForce RTX 3060
|8
|3584
|7.68
|45.92
|53.6
|AUBNEW1DQ54G3
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||NVIDIA RTX A2000||6||3328||9.1||54.4||63.5||HDR-A2000
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz ||NVIDIA GeForce RTX 4050 Laptop GPU||6||2560||7.9||59.4||67.3||AL1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA GeForce GTX 1660||6||1408||9.4||61.0||70.4||JFP
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce GTX 1650 SUPER||4||1280||10.3||64.6||74.9||TS1
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|NVIDIA RTX A2000 Laptop GPU
|4
|2560
|11.4
|72.6
|84.0
|SK1
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz (64 CPUs), ~2.3GHz||NVIDIA Tesla M60||16||4096||12||82.7||94.7||SM2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i3-8100 CPU @ 3.60GHz ||NVIDIA Tesla K40c||12||2880||12.3||83.1||95.3||NWE
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA Quadro T2000||4||1024||13.3||85.2||95.5||SSM
|-
|Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz (Laptop) ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs), ~3.8GHz
|NVIDIA Quadro T1000
|4
|896
|13.6
|91.3
|104.9
|CB4
|-
|13th Gen Intel(R) Core(TM) i7-13800H (20 CPUs), ~2.5GHz
|NVIDIA RTX A500 Laptop GPU
|4
|2048
|14.8
|93.9
|108.7
|YB1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz||NVIDIA T500||4||986||29.3||197.5||226.8||TJS
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||NVIDIA A10-4Q||24||8192||44.5||212.6||257.1||RBR
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|73.3
|568.7
|642.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||NVIDIA GeForce RTX 4090||24||16384||103.3||811.2||914.5||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz||NVIDIA GeForce RTX 4090||24||16384||105.9||813.7||919.6||TA1
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz||NVIDIA GeForce RTX 4090||24||16384||103.7||824.3||928.0||JW1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||108.2||821.5||929.7||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||106.8||875.5||982.3||CR4
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz ||NVIDIA RTX 6000 Ada Generation||48||18176||119.6||877.8||997.4||JG3
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|134.0
|1049.9
|1183.9
|TM2
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||139.5||1115.1||1254.6||JM3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||155.5||1176.2||1331.7||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||159.7||1216.2||1375.9||GP1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||160.9||1240||1400.9||DD2
|-
|Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz ||NVIDIA GeForce RTX 3090||24||10496||172.9||1249.8||1422.7||SIP
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 2080 Ti||12||7680||190.2||1493.2||1683.4||HP1
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||215.9||1678.7||1894.6||PA2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||255.4||1691.7||1947.1||HP2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|242.3
|1856.5
|2098.8
|YW1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking | Back to TUFLOW Benchmarking]]
}}

Hardware Benchmarking - Results

2026-01-06T00:37:52Z

Abrar.Alttahir: /* High End GPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable"

! style="background-color:#005581; font-weight:bold; color:white;"| Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||128||5200||32.7||108.6||141.3||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||64||5600||36.7||114.2||150.9||TM1
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||2.0||128||4000||41.2||124.1||165.3||AW1
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs)||3.2||128||4800||38.5||127.0||165.5||SB1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz||4.5||64||4800||33.4||136.0||169.4||PSM
|-
|12th Gen Intel(R) Core(TM) i9-12900K||3.2||128||3200||40.7||129.9||170.6||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz||2.1||64||4800||41||137.2||178.2||RB1
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs) @ ~4.2GHz||4.2||32||4800||35.5||162.8||198.3||LC1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs)||2.5||16||3200||48.4||170.5||218.9||TS1
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz||3.2||64||4800||53.3||169.1||222.4||CHR
|-
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|AMD Ryzen 5 7600X 6-Core Processor||4.7||32||4800||39.0||175.7||214.7||HP1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||3.6||64||2133||62.7||191.1||253.8||PA1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|3.2
|450
|
|50.0
|211.6
|261.6
|YW1
|-
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||3.2||56||N/A||49.1||218.9||268||RBR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||32||2113||68.2||217.9||286.1||HP2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||3.5||128||2666||66.3||234.7||301||JG3
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|2.6
|32
|3200
|66.6
|243.3
|309.9
|SK1
|-
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz||1.4||16||7467||173.8||152.8||326.6||AL1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||3.7||64||2933||95.4||231.3||326.7||JB2
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|AMD EPYC 7V12 64-Core Processor (16 CPUs) ||2.4||57||NA||63.5||277.6||341.1||CS2
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||3.8||64||3200||87.8||294.2||382||HDR-A2000
|-
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz ||3.0||4||986||84.0||351.4||435.4||TJS
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||3.5||16||1333||125.9||320.5||446.4||EFC
|-
|Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz||1.9||16||1800||100.1||347.0||447.1||SM1
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||4.1||16||4104||62.7||386.6||449.3||GZH
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||2.4||128||2394||130.5||331.5||462||VDI
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|-
|}
=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|2.5
|9.5
|12.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz ||NVIDIA GeForce RTX 4090||24||16384||2.4||11.6||14.0||JW1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4090||24||16384||2.5||12.1||14.6||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz ||NVIDIA GeForce RTX 4090||24||16384||2.7||12.7||15.4||TA1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||2.9||12.7||15.6||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||3.1||13.3||16.4||CR4
|-
|Intel(R) Core(TM) i9-14900K (32 CPUs) @ ~3.2GHz||NVIDIA GeForce RTX 4080 Super||16||10240||2.8||14.5||17.3||DF2
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||TM1
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||JM3
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||NVIDIA GeForce RTX 4080||16||9728||3.2||15.0||18.2||AW1
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|3.2
|15.0
|18.2
|TM2
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs)@ ~4.2GHz||NVIDIA GeForce RTX 4080 SUPER||16||10240||3.2||15.7||18.9||LC1
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||NVIDIA RTX 6000 Ada Generation||48||18176||4.9||14.2||19.1||JG3
|-
|Intel(R) Xeon(R) CPU E5-2697 v4 @ 2.30GHz (72 CPUs)||NVIDIA GeForce RTX 4090||24||16384||3.9||16.2||20.1||CS3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||3.4||19.2||22.6||HP1
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||3.8||19.2||23.0||RS1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||3.7||19.4||23.1||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700F
|NVIDIA GeForce RTX 3080 Ti
|12
|10240
|3.75
|19.57
|23.32
|AUBNE1PC6602
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||4.0||19.6||23.6||GP1
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA A40||48||10752||3.5||20.3||23.8||Lenovo SR650 V2
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.1||20.1||24.2||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz (36 CPUs), ~3.0GHz
|NVIDIA RTX A6000
|48
|10752
|4.7
|22.7
|27.4
|CB2
|-
|13th Gen Intel(R) Core(TM) i5-13600HX (20 CPUs) @ ~2.6GHz ||NVIDIA GeForce RTX 4090 Laptop GPU||16||9728||5.3||22.2||27.5||LC2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz ||NVIDIA GeForce RTX 3080||10||8704||4.3||23.7||28||RB1
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs) ||NVIDIA RTX A5000||24||8192||4.4||24.1||28.5||SB1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Xeon(R) W-2223 CPU @ 3.60GHz ||NVIDIA GeForce RTX 3090||24||10496||5.3||24.3||29.6||SSM2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen Threadripper 2920X 12-Core Processor (24 CPUs) @ 3.5GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.1||25.9||31||CR5
|-
|Intel(R) Xeon(R) W-2145 CPU @ 3.70GHz (16 CPUs), ~3.7GHz
|NVIDIA RTX A5000
|24
|8192
|5.2
|26.3
|31.5
|CB3
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.9||28.4||34.3||HP2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz ||NVIDIA GRID RTX8000P-48Q||48||4608||6.8||31.0||37.8||VJ1
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs) ||NVIDIA RTX A4000||16||6144||5.8||32.0||37.8||JS2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||6.0||32.0||38.0||EFC
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|7.2
|31.4
|38.6
|YW1
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs) @ 3.2GHz ||NVIDIA A10-24Q||24||8192||7.2||31.4||38.6||CS1
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||33.3||38.7||PA1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce RTX 3060 Ti (LHR) ||8||4864||5.7||33.3||39.0||DF1
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||NVIDIA GeForce RTX 2060 SUPER (core 1647MHz, mem 1750MHz)||8||2176||5.6||33.4||39.1||GZH
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||35.1||41.6||JB2
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz ||NVIDIA RTX A4000||16||6144||7.2||35.3||42.5||CHR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|AMD Ryzen 7 7840HS ||NVIDIA GeForce RTX 4060 Laptop GPU||8||3072||7.0||40.6||47.6||CH1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||NVIDIA GRID RTX6000P-4Q||24||4608||10.5||39.3||49.8||VDI
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA Tesla T4||16||2560||6.8||45.2||52.0||RJ1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz
|NVIDIA GeForce RTX 3060
|8
|3584
|7.68
|45.92
|53.6
|AUBNEW1DQ54G3
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||NVIDIA RTX A2000||6||3328||9.1||54.4||63.5||HDR-A2000
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz ||NVIDIA GeForce RTX 4050 Laptop GPU||6||2560||7.9||59.4||67.3||AL1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA GeForce GTX 1660||6||1408||9.4||61.0||70.4||JFP
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce GTX 1650 SUPER||4||1280||10.3||64.6||74.9||TS1
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|NVIDIA RTX A2000 Laptop GPU
|4
|2560
|11.4
|72.6
|84.0
|SK1
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz (64 CPUs), ~2.3GHz||NVIDIA Tesla M60||16||4096||12||82.7||94.7||SM2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i3-8100 CPU @ 3.60GHz ||NVIDIA Tesla K40c||12||2880||12.3||83.1||95.3||NWE
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA Quadro T2000||4||1024||13.3||85.2||95.5||SSM
|-
|Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz (Laptop) ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs), ~3.8GHz
|NVIDIA Quadro T1000
|4
|896
|13.6
|91.3
|104.9
|CB4
|-
|13th Gen Intel(R) Core(TM) i7-13800H (20 CPUs), ~2.5GHz
|NVIDIA RTX A500 Laptop GPU
|4
|2048
|14.8
|93.9
|108.7
|YB1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz||NVIDIA T500||4||986||29.3||197.5||226.8||TJS
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||NVIDIA A10-4Q||24||8192||44.5||212.6||257.1||RBR
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|73.3
|568.7
|642.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||NVIDIA GeForce RTX 4090||24||16384||103.3||811.2||914.5||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz||NVIDIA GeForce RTX 4090||24||16384||105.9||813.7||919.6||TA1
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz||NVIDIA GeForce RTX 4090||24||16384||103.7||824.3||928.0||JW1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||108.2||821.5||929.7||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||106.8||875.5||982.3||CR4
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz ||NVIDIA RTX 6000 Ada Generation||48||18176||119.6||877.8||997.4||JG3
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|134.0
|1049.9
|1183.9
|TM2
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||139.5||1115.1||1254.6||JM3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||155.5||1176.2||1331.7||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||159.7||1216.2||1375.9||GP1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||160.9||1240||1400.9||DD2
|-
|Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz ||NVIDIA GeForce RTX 3090||24||10496||172.9||1249.8||1422.7||SIP
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 2080 Ti||12||7680||190.2||1493.2||1683.4||HP1
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||215.9||1678.7||1894.6||PA2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||255.4||1691.7||1947.1||HP2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|242.3
|1856.5
|2098.8
|YW1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking | Back to TUFLOW Benchmarking]]
}}

Hardware Benchmarking - Results

2026-01-06T00:24:37Z

Abrar.Alttahir: /* High End GPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable"

! style="background-color:#005581; font-weight:bold; color:white;"| Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||128||5200||32.7||108.6||141.3||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||64||5600||36.7||114.2||150.9||TM1
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||2.0||128||4000||41.2||124.1||165.3||AW1
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs)||3.2||128||4800||38.5||127.0||165.5||SB1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz||4.5||64||4800||33.4||136.0||169.4||PSM
|-
|12th Gen Intel(R) Core(TM) i9-12900K||3.2||128||3200||40.7||129.9||170.6||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz||2.1||64||4800||41||137.2||178.2||RB1
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs) @ ~4.2GHz||4.2||32||4800||35.5||162.8||198.3||LC1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs)||2.5||16||3200||48.4||170.5||218.9||TS1
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz||3.2||64||4800||53.3||169.1||222.4||CHR
|-
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|AMD Ryzen 5 7600X 6-Core Processor||4.7||32||4800||39.0||175.7||214.7||HP1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||3.6||64||2133||62.7||191.1||253.8||PA1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|3.2
|450
|
|50.0
|211.6
|261.6
|YW1
|-
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||3.2||56||N/A||49.1||218.9||268||RBR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||32||2113||68.2||217.9||286.1||HP2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||3.5||128||2666||66.3||234.7||301||JG3
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|2.6
|32
|3200
|66.6
|243.3
|309.9
|SK1
|-
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz||1.4||16||7467||173.8||152.8||326.6||AL1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||3.7||64||2933||95.4||231.3||326.7||JB2
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|AMD EPYC 7V12 64-Core Processor (16 CPUs) ||2.4||57||NA||63.5||277.6||341.1||CS2
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||3.8||64||3200||87.8||294.2||382||HDR-A2000
|-
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz ||3.0||4||986||84.0||351.4||435.4||TJS
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||3.5||16||1333||125.9||320.5||446.4||EFC
|-
|Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz||1.9||16||1800||100.1||347.0||447.1||SM1
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||4.1||16||4104||62.7||386.6||449.3||GZH
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||2.4||128||2394||130.5||331.5||462||VDI
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|-
|}
=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|2.5
|9.5
|12.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz ||NVIDIA GeForce RTX 4090||24||16384||2.4||11.6||14.0||JW1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4090||24||16384||2.5||12.1||14.6||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz ||NVIDIA GeForce RTX 4090||24||16384||2.7||12.7||15.4||TA1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||2.9||12.7||15.6||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||3.1||13.3||16.4||CR4
|-
|Intel(R) Core(TM) i9-14900K (32 CPUs) @ ~3.2GHz||NVIDIA GeForce RTX 4080 Super||16||10240||2.8||14.5||17.3||DF2
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||TM1
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||JM3
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||NVIDIA GeForce RTX 4080||16||9728||3.2||15.0||18.2||AW1
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|3.2
|15.0
|18.2
|KW3
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs)@ ~4.2GHz||NVIDIA GeForce RTX 4080 SUPER||16||10240||3.2||15.7||18.9||LC1
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||NVIDIA RTX 6000 Ada Generation||48||18176||4.9||14.2||19.1||JG3
|-
|Intel(R) Xeon(R) CPU E5-2697 v4 @ 2.30GHz (72 CPUs)||NVIDIA GeForce RTX 4090||24||16384||3.9||16.2||20.1||CS3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||3.4||19.2||22.6||HP1
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||3.8||19.2||23.0||RS1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||3.7||19.4||23.1||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700F
|NVIDIA GeForce RTX 3080 Ti
|12
|10240
|3.75
|19.57
|23.32
|AUBNE1PC6602
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||4.0||19.6||23.6||GP1
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA A40||48||10752||3.5||20.3||23.8||Lenovo SR650 V2
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.1||20.1||24.2||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz (36 CPUs), ~3.0GHz
|NVIDIA RTX A6000
|48
|10752
|4.7
|22.7
|27.4
|CB2
|-
|13th Gen Intel(R) Core(TM) i5-13600HX (20 CPUs) @ ~2.6GHz ||NVIDIA GeForce RTX 4090 Laptop GPU||16||9728||5.3||22.2||27.5||LC2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz ||NVIDIA GeForce RTX 3080||10||8704||4.3||23.7||28||RB1
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs) ||NVIDIA RTX A5000||24||8192||4.4||24.1||28.5||SB1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Xeon(R) W-2223 CPU @ 3.60GHz ||NVIDIA GeForce RTX 3090||24||10496||5.3||24.3||29.6||SSM2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen Threadripper 2920X 12-Core Processor (24 CPUs) @ 3.5GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.1||25.9||31||CR5
|-
|Intel(R) Xeon(R) W-2145 CPU @ 3.70GHz (16 CPUs), ~3.7GHz
|NVIDIA RTX A5000
|24
|8192
|5.2
|26.3
|31.5
|CB3
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.9||28.4||34.3||HP2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz ||NVIDIA GRID RTX8000P-48Q||48||4608||6.8||31.0||37.8||VJ1
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs) ||NVIDIA RTX A4000||16||6144||5.8||32.0||37.8||JS2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||6.0||32.0||38.0||EFC
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|7.2
|31.4
|38.6
|YW1
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs) @ 3.2GHz ||NVIDIA A10-24Q||24||8192||7.2||31.4||38.6||CS1
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||33.3||38.7||PA1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce RTX 3060 Ti (LHR) ||8||4864||5.7||33.3||39.0||DF1
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||NVIDIA GeForce RTX 2060 SUPER (core 1647MHz, mem 1750MHz)||8||2176||5.6||33.4||39.1||GZH
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||35.1||41.6||JB2
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz ||NVIDIA RTX A4000||16||6144||7.2||35.3||42.5||CHR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|AMD Ryzen 7 7840HS ||NVIDIA GeForce RTX 4060 Laptop GPU||8||3072||7.0||40.6||47.6||CH1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||NVIDIA GRID RTX6000P-4Q||24||4608||10.5||39.3||49.8||VDI
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA Tesla T4||16||2560||6.8||45.2||52.0||RJ1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz
|NVIDIA GeForce RTX 3060
|8
|3584
|7.68
|45.92
|53.6
|AUBNEW1DQ54G3
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||NVIDIA RTX A2000||6||3328||9.1||54.4||63.5||HDR-A2000
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz ||NVIDIA GeForce RTX 4050 Laptop GPU||6||2560||7.9||59.4||67.3||AL1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA GeForce GTX 1660||6||1408||9.4||61.0||70.4||JFP
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce GTX 1650 SUPER||4||1280||10.3||64.6||74.9||TS1
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|NVIDIA RTX A2000 Laptop GPU
|4
|2560
|11.4
|72.6
|84.0
|SK1
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz (64 CPUs), ~2.3GHz||NVIDIA Tesla M60||16||4096||12||82.7||94.7||SM2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i3-8100 CPU @ 3.60GHz ||NVIDIA Tesla K40c||12||2880||12.3||83.1||95.3||NWE
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA Quadro T2000||4||1024||13.3||85.2||95.5||SSM
|-
|Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz (Laptop) ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs), ~3.8GHz
|NVIDIA Quadro T1000
|4
|896
|13.6
|91.3
|104.9
|CB4
|-
|13th Gen Intel(R) Core(TM) i7-13800H (20 CPUs), ~2.5GHz
|NVIDIA RTX A500 Laptop GPU
|4
|2048
|14.8
|93.9
|108.7
|YB1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz||NVIDIA T500||4||986||29.3||197.5||226.8||TJS
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||NVIDIA A10-4Q||24||8192||44.5||212.6||257.1||RBR
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|73.3
|568.7
|642.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||NVIDIA GeForce RTX 4090||24||16384||103.3||811.2||914.5||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz||NVIDIA GeForce RTX 4090||24||16384||105.9||813.7||919.6||TA1
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz||NVIDIA GeForce RTX 4090||24||16384||103.7||824.3||928.0||JW1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||108.2||821.5||929.7||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||106.8||875.5||982.3||CR4
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz ||NVIDIA RTX 6000 Ada Generation||48||18176||119.6||877.8||997.4||JG3
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|134.0
|1049.9
|1183.9
|KW3
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||139.5||1115.1||1254.6||JM3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||155.5||1176.2||1331.7||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||159.7||1216.2||1375.9||GP1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||160.9||1240||1400.9||DD2
|-
|Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz ||NVIDIA GeForce RTX 3090||24||10496||172.9||1249.8||1422.7||SIP
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 2080 Ti||12||7680||190.2||1493.2||1683.4||HP1
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||215.9||1678.7||1894.6||PA2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||255.4||1691.7||1947.1||HP2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|242.3
|1856.5
|2098.8
|YW1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking | Back to TUFLOW Benchmarking]]
}}

Hardware Benchmarking - Results

2026-01-06T00:17:37Z

Abrar.Alttahir: /* GPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable"

! style="background-color:#005581; font-weight:bold; color:white;"| Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||128||5200||32.7||108.6||141.3||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||3.2||64||5600||36.7||114.2||150.9||TM1
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||2.0||128||4000||41.2||124.1||165.3||AW1
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs)||3.2||128||4800||38.5||127.0||165.5||SB1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz||4.5||64||4800||33.4||136.0||169.4||PSM
|-
|12th Gen Intel(R) Core(TM) i9-12900K||3.2||128||3200||40.7||129.9||170.6||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz||2.1||64||4800||41||137.2||178.2||RB1
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs) @ ~4.2GHz||4.2||32||4800||35.5||162.8||198.3||LC1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs)||2.5||16||3200||48.4||170.5||218.9||TS1
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz||3.2||64||4800||53.3||169.1||222.4||CHR
|-
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|AMD Ryzen 5 7600X 6-Core Processor||4.7||32||4800||39.0||175.7||214.7||HP1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||3.6||64||2133||62.7||191.1||253.8||PA1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|3.2
|450
|
|50.0
|211.6
|261.6
|YW1
|-
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||3.2||56||N/A||49.1||218.9||268||RBR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||32||2113||68.2||217.9||286.1||HP2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||3.5||128||2666||66.3||234.7||301||JG3
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|2.6
|32
|3200
|66.6
|243.3
|309.9
|SK1
|-
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz||1.4||16||7467||173.8||152.8||326.6||AL1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||3.7||64||2933||95.4||231.3||326.7||JB2
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|AMD EPYC 7V12 64-Core Processor (16 CPUs) ||2.4||57||NA||63.5||277.6||341.1||CS2
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||3.8||64||3200||87.8||294.2||382||HDR-A2000
|-
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz ||3.0||4||986||84.0||351.4||435.4||TJS
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||3.5||16||1333||125.9||320.5||446.4||EFC
|-
|Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz||1.9||16||1800||100.1||347.0||447.1||SM1
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||4.1||16||4104||62.7||386.6||449.3||GZH
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||2.4||128||2394||130.5||331.5||462||VDI
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|-
|}
=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|2.5
|9.5
|12.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz ||NVIDIA GeForce RTX 4090||24||16384||2.4||11.6||14.0||JW1
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4090||24||16384||2.5||12.1||14.6||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz ||NVIDIA GeForce RTX 4090||24||16384||2.7||12.7||15.4||TA1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||2.9||12.7||15.6||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||3.1||13.3||16.4||CR4
|-
|Intel(R) Core(TM) i9-14900K (32 CPUs) @ ~3.2GHz||NVIDIA GeForce RTX 4080 Super||16||10240||2.8||14.5||17.3||DF2
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||TM1
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||2.8||14.7||17.5||JM3
|-
|13th Gen Intel(R) Core(TM) i9-13900F (32 CPUs) @ 2.0GHz ||NVIDIA GeForce RTX 4080||16||9728||3.2||15.0||18.2||AW1
|-
|AMD Ryzen 9 9900X 12-Core Processor (24 CPUs), ~4.4GHz
|NVIDIA GeForce RTX 5070 Ti
|16
|8960
|3.2
|15.0
|18.2
|KW3
|-
|AMD Ryzen 7 7800X3D 8-Core Processor (16 CPUs)@ ~4.2GHz||NVIDIA GeForce RTX 4080 SUPER||16||10240||3.2||15.7||18.9||LC1
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz||NVIDIA RTX 6000 Ada Generation||48||18176||4.9||14.2||19.1||JG3
|-
|Intel(R) Xeon(R) CPU E5-2697 v4 @ 2.30GHz (72 CPUs)||NVIDIA GeForce RTX 4090||24||16384||3.9||16.2||20.1||CS3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 4070 Ti||12||7680||3.4||19.2||22.6||HP1
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||3.8||19.2||23.0||RS1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||3.7||19.4||23.1||DD2
|-
|12th Gen Intel(R) Core(TM) i7-12700F
|NVIDIA GeForce RTX 3080 Ti
|12
|10240
|3.75
|19.57
|23.32
|AUBNE1PC6602
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||4.0||19.6||23.6||GP1
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA A40||48||10752||3.5||20.3||23.8||Lenovo SR650 V2
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.1||20.1||24.2||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz (36 CPUs), ~3.0GHz
|NVIDIA RTX A6000
|48
|10752
|4.7
|22.7
|27.4
|CB2
|-
|13th Gen Intel(R) Core(TM) i5-13600HX (20 CPUs) @ ~2.6GHz ||NVIDIA GeForce RTX 4090 Laptop GPU||16||9728||5.3||22.2||27.5||LC2
|-
|12th Gen Intel(R) Core(TM) i7-12700 (20 CPUs) @ ~2.1GHz ||NVIDIA GeForce RTX 3080||10||8704||4.3||23.7||28||RB1
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|12th Gen Intel(R) Core(TM) i9-12900K (24 CPUs) ||NVIDIA RTX A5000||24||8192||4.4||24.1||28.5||SB1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Xeon(R) W-2223 CPU @ 3.60GHz ||NVIDIA GeForce RTX 3090||24||10496||5.3||24.3||29.6||SSM2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen Threadripper 2920X 12-Core Processor (24 CPUs) @ 3.5GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.1||25.9||31||CR5
|-
|Intel(R) Xeon(R) W-2145 CPU @ 3.70GHz (16 CPUs), ~3.7GHz
|NVIDIA RTX A5000
|24
|8192
|5.2
|26.3
|31.5
|CB3
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.9||28.4||34.3||HP2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz ||NVIDIA GRID RTX8000P-48Q||48||4608||6.8||31.0||37.8||VJ1
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs) ||NVIDIA RTX A4000||16||6144||5.8||32.0||37.8||JS2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||6.0||32.0||38.0||EFC
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|7.2
|31.4
|38.6
|YW1
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs) @ 3.2GHz ||NVIDIA A10-24Q||24||8192||7.2||31.4||38.6||CS1
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||33.3||38.7||PA1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce RTX 3060 Ti (LHR) ||8||4864||5.7||33.3||39.0||DF1
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||NVIDIA GeForce RTX 2060 SUPER (core 1647MHz, mem 1750MHz)||8||2176||5.6||33.4||39.1||GZH
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz (20 CPUs)||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||35.1||41.6||JB2
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Xeon(R) w5-2455X (24 CPUs) @ ~3.2GHz ||NVIDIA RTX A4000||16||6144||7.2||35.3||42.5||CHR
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|AMD Ryzen 7 7840HS ||NVIDIA GeForce RTX 4060 Laptop GPU||8||3072||7.0||40.6||47.6||CH1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz ||NVIDIA GRID RTX6000P-4Q||24||4608||10.5||39.3||49.8||VDI
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Xeon(R) Gold 5317 CPU @ 3.00GHz (48 CPUs) ||NVIDIA Tesla T4||16||2560||6.8||45.2||52.0||RJ1
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz
|NVIDIA GeForce RTX 3060
|8
|3584
|7.68
|45.92
|53.6
|AUBNEW1DQ54G3
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Xeon(R) W-2235 CPU @ 3.80GHz (12 CPUs)||NVIDIA RTX A2000||6||3328||9.1||54.4||63.5||HDR-A2000
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Core(TM) Ultra 7 155H (22 CPUs) @ 1.4GHz ||NVIDIA GeForce RTX 4050 Laptop GPU||6||2560||7.9||59.4||67.3||AL1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA GeForce GTX 1660||6||1408||9.4||61.0||70.4||JFP
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz (16 CPUs) ||NVIDIA GeForce GTX 1650 SUPER||4||1280||10.3||64.6||74.9||TS1
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz (16 CPUs), ~2.6GHz
|NVIDIA RTX A2000 Laptop GPU
|4
|2560
|11.4
|72.6
|84.0
|SK1
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz (64 CPUs), ~2.3GHz||NVIDIA Tesla M60||16||4096||12||82.7||94.7||SM2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i3-8100 CPU @ 3.60GHz ||NVIDIA Tesla K40c||12||2880||12.3||83.1||95.3||NWE
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA Quadro T2000||4||1024||13.3||85.2||95.5||SSM
|-
|Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz (Laptop) ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz (16 CPUs), ~3.8GHz
|NVIDIA Quadro T1000
|4
|896
|13.6
|91.3
|104.9
|CB4
|-
|13th Gen Intel(R) Core(TM) i7-13800H (20 CPUs), ~2.5GHz
|NVIDIA RTX A500 Laptop GPU
|4
|2048
|14.8
|93.9
|108.7
|YB1
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Core(TM) i7-1185G7 @ 3.00GHz||NVIDIA T500||4||986||29.3||197.5||226.8||TJS
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|AMD EPYC 74F3 24-Core Processor (6 CPUs) @ 3.2GHz||NVIDIA A10-4Q||24||8192||44.5||212.6||257.1||RBR
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|AMD Ryzen 9 9950X3D 16-Core Processor (32 CPUs), ~4.3GHz
|NVIDIA GeForce RTX 5090
|32
|21760
|73.3
|568.7
|642.0
|JW3
|-
|13th Gen Intel(R) Core(TM) i9-13900KS (32 CPUs) @ 3.2GHz||NVIDIA GeForce RTX 4090||24||16384||103.3||811.2||914.5||JB1
|-
|13th Gen Intel(R) Core(TM) i9-13900K (32 CPUs) @ 3.0GHz||NVIDIA GeForce RTX 4090||24||16384||105.9||813.7||919.6||TA1
|-
|13th Gen Intel(R) Core(TM) i7-13700KF (24 CPUs), @ 3.4GHz||NVIDIA GeForce RTX 4090||24||16384||103.7||824.3||928.0||JW1
|-
|AMD Ryzen 9 7950X 16-Core Processor (32 CPUs) @ 4.5GHz ||NVIDIA GeForce RTX 4090||24||16384||108.2||821.5||929.7||PSM
|-
|AMD Ryzen 9 3900X 12-Core Processor (24 CPUs) @ 3.8GHz ||NVIDIA GeForce RTX 4090||24||16384||106.8||875.5||982.3||CR4
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor (32 CPUs) @ 3.5GHz ||NVIDIA RTX 6000 Ada Generation||48||18176||119.6||877.8||997.4||JG3
|-
|12th Gen Intel(R) Core(TM) i9-12900 (24 CPUs) @ 2.4GHz ||NVIDIA GeForce RTX 4080||16||9728||139.5||1115.1||1254.6||JM3
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||155.5||1176.2||1331.7||JMM
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|AMD Ryzen 9 5900X 12-Core Processor ||NVIDIA GeForce RTX 3080 Ti||12||10240||159.7||1216.2||1375.9||GP1
|-
|12th Gen Intel(R) Core(TM) i9-12900K ||NVIDIA GeForce RTX 3090||24||10496||160.9||1240||1400.9||DD2
|-
|Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz ||NVIDIA GeForce RTX 3090||24||10496||172.9||1249.8||1422.7||SIP
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen 5 7600X 6-Core Processor ||NVIDIA GeForce RTX 2080 Ti||12||7680||190.2||1493.2||1683.4||HP1
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz (8 CPUs), ~3.6GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||215.9||1678.7||1894.6||PA2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||255.4||1691.7||1947.1||HP2
|-
|AMD EPYC 74F3 24-Core Processor (36 CPUs), ~3.2GHz
|NVIDIA A10-24Q
|22
|9216
|242.3
|1856.5
|2098.8
|YW1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking | Back to TUFLOW Benchmarking]]
}}

Tutorial M09

2026-01-05T05:13:02Z

Abrar.Alttahir: /* Variables (Optional) */

= Introduction =
In this module, event management is introduced. This is a powerful functionality running multiple event combinations (e.g. magnitude, duration, temporal patterns, climate change) using a single set of control files rather than creating a new set of control files for every simulation. This makes the management of the model easier, ensures consistency between the simulations and better quality control. Without implementing event management, six TCFs and six bc_dbase files are required to run this module, using event management, only one each is needed:

 
[[File:M09 EventManagement 01a.png]] Events are set up using a define event block in TUFLOW Event File (TEF) - <tt>Define Event</tt> command terminated by <tt>End Define</tt>.

Note: If Event logic can be added into most of TUFLOW’s control files where model inputs or parameters may vary between events. The only control files not supporting event logic are the TUFLOW Rainfall Control File (.trcf), TUFLOW External Stress File (.tesf), Advection Control File (.adcf) and within a Define Control command inside a TUFLOW Operational Control (.toc) file.

The TUFLOW control files are:
:*TUFLOW Events File (TEF): A database of TCF and ECF commands for different events. 

'''Module 9 builds from the model created in [[Tutorial_M02 | Module 2]]. The completed Module 2 model is provided in the Module_09\TUFLOW folder.'''

= Simulation Control Files =
=== TUFLOW Event File (TEF) ===
<ol>
<li>Navigate to the '''Module_09\Tutorial_Data''' folder. Copy the '''Event_File.tef''' in to the ''' Module_09\TUFLOW\runs''' folder.
<li>Open the TEF in a text editor. There are three event magnitudes and two event durations listed:
:*Event Magnitudes: 5% AEP, 2% AEP and 1% AEP
:*Event Durations: 1hr and 2hr
<li>The event specific commands are contained between the <tt>Define Event</tt> and <tt>End Define</tt> commands.
<li>The <tt>BC Event Source</tt> command is linked to the bc_dbase file. In the below examples, any occurrence of '~AEP~' in the bc_dbase is replaced with '05p' and any occurrence of '~DUR~' in the bc_dbase is replaced with '1hr'. 
<tt>! EVENT MAGNITUDES</tt> 
<tt>Define Event</tt> <tt> == </tt> <tt>05p</tt>
<ol><tt> BC Event Source </tt> <tt>==</tt> <tt> ~AEP~ | 05p</tt>
</ol><tt>End Define</tt> 
<tt>! EVENT DURATIONS</tt> 
<tt>Define Event</tt> <tt> == </tt> <tt>1hr</tt>
<ol><tt> BC Event Source </tt> <tt>==</tt> <tt> ~DUR~ | 1hr</tt>
</ol><tt>End Define</tt>
Note: The 'AEP' and 'DUR' are user defined variables. The 'AEP' refers to an event magnitude (Annual Exceedance Probability) and 'DUR' refers to an event duration. 
<li>TUFLOW reads all of the defined events in the TEF and selects the ones specified in the batch file. 
</ol>

=== TUFLOW Boundary Condition Database (bc_dbase) ===
Update the bc_dbase with event magnitude and event duration placeholders:
<ol>
<li>Navigate to the '''Module_09\Tutorial_Data''' folder. Copy and paste the following files to the ''' Module_09\TUFLOW\bc_dbase''' folder. These files contains the inflows for each event magnitude and duration.
:*05p1hr.csv
:*05p2hr.csv
:*02p1hr.csv
:*02p2hr.csv
:*01p1hr.csv
:*01p2hr.csv
<li>Save a copy of the '''bc_dbase.csv''' as '''bc_dbase_M09_001.csv'''.
<li>Open the file and make the following updates to the 'Source' column: 
 
[[File:M09 bc dbase 02.png]] 
 
<li>Save the bc_dbase.
</ol>

=== TUFLOW Control File (TCF) ===
<ol>
<li>Save a copy of '''M02_5m_001.tcf''' as '''M09_5m_~e1~_~e2~_001.tcf''' in the '''Module_09\TUFLOW\runs''' folder.
<li>Open the file '''M09_5m_~e1~_~e2~_001.tcf''' in a text editor.
<li>Update the reference to the bc_dbase: 
<tt>BC Database </tt> <tt>== </tt> <tt> ..\bc_dbase\bc_dbase_M09_001.csv </tt> <tt> ! Reference the Boundary Conditions Database</tt> 
<li>Add the following command to reference the TEF at the end of the TCF: 
<tt>! EVENTS</tt> 
<tt>Event File </tt> <tt>== </tt> <tt> Event_File.tef </tt> <tt> ! Reference the TUFLOW Event File</tt> 
<li>Save the TCF.
</ol>
 

= Running the Simulation =
<ol>
<li>Save a copy of '''_run_M02_HPC.bat''' as '''_run_M09_HPC.bat''' in the '''Module_09\TUFLOW\runs''' folder.
<li>Update the batch file to reference the '''M09_5m_~e1~_~e2~_001.tcf''' and include event switches (-e1, -e2) defining event magnitude and event duration: 
<tt>'''set'''</tt> <tt>exe</tt><tt>=</tt><tt>"..\..\..\exe\2025.1.0\TUFLOW_iSP_w64.exe"</tt> 
<tt>'''set'''</tt> <tt>run</tt><tt>=</tt><tt>start "TUFLOW" /wait</tt> <tt> %exe%</tt> <tt> -b</tt> 
<tt>%run% </tt> <tt> -e1 05p -e2 1hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<tt>%run% </tt> <tt> -e1 05p -e2 2hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<tt>%run% </tt> <tt> -e1 02p -e2 1hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<tt>%run% </tt> <tt> -e1 02p -e2 2hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<tt>%run% </tt> <tt> -e1 01p -e2 1hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<tt>%run% </tt> <tt> -e1 01p -e2 2hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<li>Save the batch file and double click it in file explorer to run the simulation. 
</ol>
 

= Troubleshooting =
See tips on common mistakes and troubleshooting steps if the model doesn't run:
:*[[Tutorial_Troubleshooting_QGIS | QGIS]]
 

= Check Files =
While the model is running, review the added features are specified correctly:
<ol>
<li>Open the '''M09_5m_05p_1hr_001.tlf''' from the '''Module_09\TUFLOW\runs\log''' folder in a text editor.
<li>The name of the events replaced the placeholders in the TCF name.
<li>Confirm the specified events and event sources: 
{{Video|name=Animation_M09_Check_01c.mp4|width=928}}
 
</ol>

=Results=
When the model is finished, review the results:
:*[[Tutorial_M09_Results_QGIS | QGIS]]
 

= Conclusion =

* Multiple events were set up in the TUFLOW Event File (TEF) with define event blocks.

*TUFLOW Log Files (TLF) were reviewed.
*Multiple event results were inspected using the TUFLOW Viewer.
*For further training opportunities see [https://tuflow.com/training/training-course-catalogue/ TUFLOW Training Catalogue] and/or contact [mailto:training@tuflow.com training@tuflow.com].
*Alternatively, see the [[TUFLOW_Example_Models#Example_Model_Catalogue | TUFLOW Example Models]] to explore the full list of TUFLOW features.
=Looping Batch File (Optional)=
Looping batch file is a powerful tool running large number of simulations with the least amount of batch file text:
<ol>
<li>Save a copy of '''_run_M09_HPC.bat''' as '''_run_M09_HPC_loop.bat''' in the '''Module_09\TUFLOW\runs''' folder.
<li>Add the following commands. Indentation is important in batch files, insert tabs to match the below spacing: 
<tt>echo </tt> <tt>off</tt> 
<tt>set </tt> <tt>exe</tt><tt>=</tt><tt>"..\..\..\exe\2025.1.0\TUFLOW_iSP_w64.exe"</tt> 
<tt>set </tt> <tt>run</tt><tt>=</tt><tt>start "TUFLOW" /wait </tt><tt>%exe%</tt><tt> -b</tt> 
 
<tt>set</tt> <tt> AEP</tt><tt>=</tt><tt>05p 02p 01p</tt> 
<tt>set</tt> <tt> DUR</tt><tt>=</tt><tt>1hr 2hr</tt> 
 
<tt>for </tt><tt>%%a</tt><tt> in </tt><tt>(</tt><tt>%AEP%</tt><tt>)</tt><tt> do </tt><tt>(</tt> 
<ol><tt> for </tt><tt>%%b</tt><tt> in </tt><tt>(</tt><tt>%DUR%</tt><tt>)</tt><tt> do </tt><tt>( </tt> 
<ol><ol><tt> echo </tt><tt>Running </tt><tt>%%a %%b </tt> 
<tt> %run% </tt><tt>-e1 </tt><tt>%%a</tt><tt> -e2 </tt><tt>%%b </tt><tt>M09_5m_~e1~_~e2~_001.tcf</tt> 
</ol></ol> ) 
</ol>) 
Note: The 'AEP' and 'DUR' are user defined batch file variables. The 'AEP' refers to an event magnitude (Annual Exceedance Probability) and 'DUR' refers to an event duration.

<li>Save the batch file and double click it in file explorer to run the simulation.
</ol>

= Variables (Optional) =
Variables provide a way to define values once and reuse them across control files, which helps manage models with multiple scenarios and events.

Scenarios and events are automatically available as variables. Variables can also be defined using the <tt>Set Variable </tt>command as described in Section 13.2.3 Variables of the TUFLOW manual. They are referenced by enclosing the variable name within << and >>.

Note: The <tt>Set Variable </tt>command can only be specified in the .tcf or within a read file (.trd) referenced from the .tcf. The variables can then be used in all control files (.tcf, .tgc, .tbc, .ecf, etc).

Example models using variables are provided in the [[TUFLOW Example Models#Bulk Simulation Management|Bulk Simulation Management Example Model Dataset]] on the TUFLOW Wiki.

 

{{Tips Navigation
|uplink=[[Tutorial_Introduction| Back to Tutorial Introduction Main Page]]
}}

Tutorial M08

2026-01-05T05:12:58Z

Abrar.Alttahir: /* Variables (Optional) */

= Introduction =
In this module, scenario management is introduced. This is a powerful functionality running different simulations using a single set of control files rather than creating a new set of control files for every simulation. This makes the management of the model easier, ensures consistency between the simulations and better quality control. Without implementing scenario management, six TCFs and six TGC files are required to run this module, using scenario management, only one each is needed: 
 
[[File:M08 ScenarioManagement 02.png]] Scenarios are set up using an if scenario logic block - <tt>If Scenario</tt> command followed by <tt>Else If Scenario</tt> commands adding on different scenarios and terminated by <tt>End If</tt>.

Note: Scenarios can be added to most of TUFLOW’s control files. The only control files that do not support scenarios are the TUFLOW Rainfall Control File (.trcf), TUFLOW External Stress File (.tesf), Advection Control File (.adcf), or within a Define Control command inside a TUFLOW Operational Control (.toc) file.

The tutorial demonstrates two ways of using scenarios:
:*Part 1: Single Scenario - cell size.
:*Part 2: Multiple Scenarios - cell size and topography updates.

The GIS layers are:
:*TGC layers:
<ol><ol><li>2d_zsh: A layer used to modify Zpt elevations using points, lines and polygons.
<li>2d_mat: A layer used to define the land use (material) types within the developmental area.</ol></ol>

'''Module 8 builds from the model created in [[Tutorial_M02 | Module 2]]. The completed Module 2 model is provided in the Module_08\TUFLOW folder.'''

=Part 1 - Single Scenario=
Use scenario management to run the model with a range of different cell sizes, initial timesteps and SGS sample frequency within a single TCF. These steps are often used to conduct a cell size convergence sensitivity testing.

== Simulation Control Files ==
=== TUFLOW Geometry Control File (TGC) ===
<ol>
<li>Save a copy of '''M02_001.tgc''' as '''M08_001.tgc''' in the '''Module_08\TUFLOW\model''' folder. 
<li>Open the '''M08_001.tgc''' in a text editor.
<li>Comment out or delete the 'cell size' command and add the following if scenario logic block: 
<tt>! Cell Size == 5 ! 2D cell size in metres</tt> 
<tt>If Scenario </tt> <tt>== </tt> <tt>10m </tt> <tt> ! Beginning of if statement - 10m scenario</tt> 
<ol><tt>Cell Size </tt> <tt>== </tt> <tt>10 </tt> <tt> ! 2D cell size in metres - 10 metres</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>5m </tt> <tt> ! 5m scenario</tt> 
<ol><tt>Cell Size </tt> <tt>== </tt> <tt>5 </tt> <tt> ! 2D cell size in metres - 5 metres</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>2.5m </tt> <tt> ! 2.5m scenario</tt> 
<ol><tt>Cell Size </tt> <tt>== </tt> <tt>2.5 </tt> <tt> ! 2D cell size in metres - 2.5 metres</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

<li>Save the TGC.
</ol>
Note: The <tt>Pause </tt> command causes TUFLOW to stop every time it encounters it. In the example above, if a cell size was specified other than 10m, 5m, or 2.5m, 'Error: Scenario Name Not Valid' would be displayed. The simulation can then be continued or discontinued via a dialog window.

=== TUFLOW Control File (TCF) ===
<ol>
<li>Save a copy of '''M02_5m_001.tcf''' as '''M08_~s1~_001.tcf''' in the '''Module_08\TUFLOW\runs''' folder.
<li>Open the file '''M08_~s1~_001.tcf''' in a text editor.

<li>Update the TGC reference: 
<tt>Geometry Control File </tt> <tt>== </tt> <tt>..\model\M08_001.tgc </tt> <tt> ! Reference the TUFLOW Geometry Control File</tt> 
<li>The Timestep is dependent on the model cell size. A general guide is to use 1/2 to 1/5 of the model cell size in metres. Comment out or delete the 'Timestep' command and add the following if scenario logic block: 
<tt>! Timestep == 1 ! Specifies the first 2D computational timestep of 1 second</tt> 
<tt>If Scenario </tt> <tt>== </tt> <tt>10m </tt> <tt> ! Beginning of if statement - 10m scenario</tt> 
<ol><tt>Timestep </tt> <tt>== </tt> <tt>2 </tt> <tt> ! Specifies the first 2D computational timestep of 2 seconds</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>5m </tt> <tt> ! 5m scenario</tt> 
<ol><tt>Timestep </tt> <tt>== </tt> <tt>1 </tt> <tt> ! Specifies the first 2D computational timestep of 1 second</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>2.5m </tt> <tt> ! 2.5m scenario</tt> 
<ol><tt>Timestep </tt> <tt>== </tt> <tt>0.5 </tt> <tt> ! Specifies the first 2D computational timestep of 0.5 seconds</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

<li>Save the TCF.
</ol>

== Running the Simulation ==
<ol>
<li>Save a copy of '''_run_M02_HPC.bat''' as '''_run_M08_HPC.bat''' in the '''Module_08\TUFLOW\runs''' folder.
<li>Update the batch file to reference the '''M08_~s1~_001.tcf''' and include a scenario switch (-s1) defining cell size: 
<tt>'''set'''</tt> <tt>exe</tt><tt>=</tt><tt>"..\..\..\exe\2025.1.0\TUFLOW_iSP_w64.exe"</tt> 
<tt>'''set'''</tt> <tt>run</tt><tt>=</tt><tt>start "TUFLOW" /wait</tt> <tt> %exe%</tt> <tt> -b</tt> 
<tt>%run% </tt> <tt> -s1 10m M08_~s1~_001.tcf </tt> 
<tt>%run% </tt> <tt> -s1 5m M08_~s1~_001.tcf </tt> 
<tt>%run% </tt> <tt> -s1 2.5m M08_~s1~_001.tcf </tt> 
<li>Save the batch file and double click it in file explorer to run the simulation.
</ol>

== Troubleshooting ==
See tips on common mistakes and troubleshooting steps if the model doesn't run:
:*[[Tutorial_Troubleshooting_QGIS | QGIS]]

== Check Files ==
While the model is running, review the added features are specified correctly:
:*[[Tutorial_M08_001_Check_Files_QGIS | QGIS - SHP]]
:*[[Tutorial_M08_001_Check_Files_QGIS_GPKG | QGIS - GPKG]]
Compare the model cell count:
<ol>
<li>Navigate to the '''Module_08\TUFLOW\runs\log''' folder and open the following files in a text editor:
:*'''M08_10m_001.tlf'''
:*'''M08_5m_001.tlf'''
:*'''M08_2.5m_001.tlf'''
<li>Search for 'Number 2D Cells' and view the number of active cells for each model. This is the number of active cells as specified by the 2d_code layer. There are approximately 16 times more cells in the 2.5m model compared to the 10m model:
:*M08_10m_001: 5122
:*M08_5m_001: 20486
:*M08_2.5m_001: 81869
</ol>

== Results==
When the model is finished, review the results:
:*[[Tutorial_M08_001_Results_QGIS | QGIS]]
Compare the model run times:
<ol>
<li>Navigate to the '''Module_08\TUFLOW\runs''' folder and open the '''_TUFLOW Simulations.log''' in a text editor.
<li>The file shows the simulation times for all the simulations.
 
[[File:M08_TUFLOW_Simulations_f.png]] 
 
</ol>
 

=Part 2 - Multiple Scenarios=
Use scenario management to run the model with a range of different cell sizes and topography changes within a single TCF. These steps are often used to conduct an impact assessment.
 

== GIS Inputs ==
Create, import and view input data: 
:*[[Tutorial_M08_002_GIS_Inputs_QGIS | QGIS - SHP]] 
:*[[Tutorial_M08_002_GIS_Inputs_QGIS_GPKG | QGIS - GPKG]] 

== Simulation Control Files ==
=== TUFLOW Geometry Control File (TGC) ===
<ol>
<li>Save a copy of '''M08_001.tgc''' as '''M08_002.tgc''' in the '''Module_08\TUFLOW\model''' folder. 
<li>Open the '''M08_002.tgc''' in a text editor.
<li>Include the following if scenario logic block for existing and developed case after the '<tt>Read GIS Z Shape</tt>' command: 

'''QGIS - SHP''' 
<tt>If Scenario </tt> <tt>== </tt> <tt>EXG </tt> <tt> ! Beginning of if statement - existing scenario</tt> 
<ol><tt> ! Defines a placeholder</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>DEV </tt> <tt> ! Development scenario</tt> 
<ol><tt>Read GIS Z Shape </tt> <tt>== </tt> <tt>gis\2d_zsh_M02_landscape_002_R.shp | gis\2d_zsh_M02_landscape_002_L.shp | gis\2d_zsh_M02_landscape_002_P.shp </tt> <tt> ! Defines areas of complex landscaping</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

'''QGIS - GPKG''' 
<tt>If Scenario </tt> <tt>== </tt> <tt>EXG </tt> <tt> ! Beginning of if statement - existing scenario</tt> 
<ol><tt> ! Defines a placeholder</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>DEV </tt> <tt> ! Development scenario</tt> 
<ol><tt>Read GIS Z Shape </tt> <tt>== </tt> <tt>2d_zsh_M02_landscape_002_R | 2d_zsh_M02_landscape_002_L | 2d_zsh_M02_landscape_002_P </tt> <tt> ! Defines areas of complex landscaping</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

<li>Include the following if scenario logic block for existing and developed case after the '<tt>Read GIS Mat</tt>' command: 

'''QGIS - SHP''' 
<tt>If Scenario </tt> <tt>== </tt> <tt>EXG </tt> <tt> ! Beginning of if statement - existing scenario</tt> 
<ol><tt> ! Defines a placeholder</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>DEV </tt> <tt> ! Development scenario</tt> 
<ol><tt>Read GIS Mat </tt> <tt>== </tt> <tt>gis\2d_mat_M02_landscape_002_R.shp </tt> <tt> ! Sets material values according to attributes in the GIS layer</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

'''QGIS - GPKG''' 
<tt>If Scenario </tt> <tt>== </tt> <tt>EXG </tt> <tt> ! Beginning of if statement - existing scenario</tt> 
<ol><tt> ! Defines a placeholder</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>DEV </tt> <tt> ! Development scenario</tt> 
<ol><tt>Read GIS Mat </tt> <tt>== </tt> <tt>2d_mat_M02_landscape_002_R </tt> <tt> ! Sets material values according to attributes in the GIS layer</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

<li>Save the TGC.
</ol>

=== TUFLOW Control File (TCF) ===
<ol>
<li>Save a copy of '''M08_~s1~_001.tcf''' as '''M08_~s1~_~s2~_002.tcf''' in the '''Module_08\TUFLOW\runs''' folder.
<li>Open the file '''M08_~s1~_~s2~_002.tcf''' in a text editor.
<li>Update the TGC reference: 
<tt>Geometry Control File </tt> <tt>== </tt> <tt>..\model\M08_002.tgc </tt> <tt> ! Reference the TUFLOW Geometry Control File</tt> 
<li>For GeoPackage users, update the Spatial Database reference: 
'''QGIS - GPKG''' 
<tt>Spatial Database </tt> <tt>== </tt> <tt>..\model\gis\M08_002.gpkg</tt> <tt> ! Specify the location of the GeoPackage Spatial Database</tt> 
<li>Save the TCF.
</ol>

== Running the Simulation ==
<ol>
<li>Open '''_run_M08_HPC.bat''' from the '''Module_08\TUFLOW\runs''' folder.
<li>Update the batch file to reference the '''M08_~s1~_~s2~_002.tcf''' and in addition to cell size scenario switch (-s1) include second scenario switch (-s2) defining topography: 
<tt>'''set'''</tt> <tt>exe</tt><tt>=</tt><tt>"..\..\..\exe\2025.1.0\TUFLOW_iSP_w64.exe"</tt> 
<tt>'''set'''</tt> <tt>run</tt><tt>=</tt><tt>start "TUFLOW" /wait</tt> <tt> %exe%</tt> <tt> -b</tt> 
<tt>%run% </tt> <tt> -s1 2.5m -s2 EXG M08_~s1~_~s2~_002.tcf </tt> 
<tt>%run% </tt> <tt> -s1 2.5m -s2 DEV M08_~s1~_~s2~_002.tcf </tt> 
<tt>%run% </tt> <tt> -s1 5m -s2 EXG M08_~s1~_~s2~_002.tcf </tt> 
<tt>%run% </tt> <tt> -s1 5m -s2 DEV M08_~s1~_~s2~_002.tcf </tt> 
<tt>%run% </tt> <tt> -s1 10m -s2 EXG M08_~s1~_~s2~_002.tcf </tt> 
<tt>%run% </tt> <tt> -s1 10m -s2 DEV M08_~s1~_~s2~_002.tcf </tt> 
<li>Save the batch file and double click it in file explorer to run the simulation. 
</ol>

== Troubleshooting ==
See tips on common mistakes and troubleshooting steps if the model doesn't run:
:*[[Tutorial_Troubleshooting_QGIS | QGIS]]

== Check Files ==
While the model is running, review the added features are specified correctly:
:*[[Tutorial_M08_002_Check_Files_QGIS | QGIS]]

== Results==
When the model is finished, review the results:
:*[[Tutorial_M08_002_Results_QGIS | QGIS]]
 

= Conclusion =
:*Single and multiple scenarios were set up and run with a range of cell sizes and designs.
:*Check files were assessed to view the changes based on each scenario.
:*Multiple scenario results were viewed using the TUFLOW Viewer.
:*For further training opportunities see [https://tuflow.com/training/training-course-catalogue/ TUFLOW Training Catalogue] and/or contact [mailto:training@tuflow.com training@tuflow.com].
:*Alternatively, see the [[TUFLOW_Example_Models#Example_Model_Catalogue | TUFLOW Example Models]] to explore the full list of TUFLOW features.
=Looping Batch File (Optional)=
Looping batch file is a powerful tool running large number of simulations with the least amount of batch file text:
<ol>
<li>Save a copy of '''_run_M08_HPC.bat''' as '''_run_M08_HPC_loop.bat''' in the '''Module_08\TUFLOW\runs''' folder.
<li>Add the following commands. Indentation is important in batch files, insert tabs to match the below spacing: 
<tt>echo </tt> <tt>off</tt> 
<tt>set </tt> <tt>exe</tt><tt>=</tt><tt>"..\..\..\exe\2025.1.0\TUFLOW_iSP_w64.exe"</tt> 
<tt>set </tt> <tt>run</tt><tt>=</tt><tt>start "TUFLOW" /wait </tt><tt>%exe%</tt><tt> -b</tt> 
 
<tt>set</tt> <tt> CSZ</tt><tt>=</tt><tt>10m 5m 2.5m</tt> 
<tt>set</tt> <tt> DES</tt><tt>=</tt><tt>EXG DEV</tt> 
 
<tt>for </tt><tt>%%a</tt><tt> in </tt><tt>(</tt><tt>%CSZ%</tt><tt>)</tt><tt> do </tt><tt>(</tt> 
<ol><tt> for </tt><tt>%%b</tt><tt> in </tt><tt>(</tt><tt>%DES%</tt><tt>)</tt><tt> do </tt><tt>( </tt> 
<ol><ol><tt> echo </tt><tt>Running </tt><tt>%%a %%b </tt> 
<tt> %run% </tt><tt>-s1 </tt><tt>%%a</tt><tt> -s2 </tt><tt>%%b </tt><tt>M08_~s1~_~s2~_002.tcf</tt> 
</ol></ol> ) 
</ol>) 
Note: The 'CSZ' and 'DES' are user defined batch file variables. The 'CSZ' refers to a cell size and 'DES' refers to a design scenario. 

<li>Save the batch file and double click it in file explorer to run the simulation.
</ol>

= Variables (Optional) =
Variables provide a way to define values once and reuse them across control files, which helps manage models with multiple scenarios and events.

Scenarios and events are automatically available as variables. Variables can also be defined using the <tt>Set Variable </tt>command as described in Section 13.2.3 Variables of the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW manual]. They are referenced by enclosing the variable name within << and >>.

Note: The <tt>Set Variable </tt>command can only be specified in the .tcf or within a read file (.trd) referenced from the .tcf. The variables can then be used in all control files (.tcf, .tgc, .tbc, .ecf, etc).

Example models using variables are provided in the [[TUFLOW Example Models#Bulk Simulation Management|Bulk Simulation Management Example Model Dataset]] on the TUFLOW Wiki.

 

{{Tips Navigation
|uplink=[[Tutorial_Introduction| Back to Tutorial Introduction Main Page]]
}}

Tutorial M09

2026-01-05T04:52:08Z

Abrar.Alttahir: /* Variables (Optional) */

= Introduction =
In this module, event management is introduced. This is a powerful functionality running multiple event combinations (e.g. magnitude, duration, temporal patterns, climate change) using a single set of control files rather than creating a new set of control files for every simulation. This makes the management of the model easier, ensures consistency between the simulations and better quality control. Without implementing event management, six TCFs and six bc_dbase files are required to run this module, using event management, only one each is needed:

 
[[File:M09 EventManagement 01a.png]] Events are set up using a define event block in TUFLOW Event File (TEF) - <tt>Define Event</tt> command terminated by <tt>End Define</tt>.

Note: If Event logic can be added into most of TUFLOW’s control files where model inputs or parameters may vary between events. The only control files not supporting event logic are the TUFLOW Rainfall Control File (.trcf), TUFLOW External Stress File (.tesf), Advection Control File (.adcf) and within a Define Control command inside a TUFLOW Operational Control (.toc) file.

The TUFLOW control files are:
:*TUFLOW Events File (TEF): A database of TCF and ECF commands for different events. 

'''Module 9 builds from the model created in [[Tutorial_M02 | Module 2]]. The completed Module 2 model is provided in the Module_09\TUFLOW folder.'''

= Simulation Control Files =
=== TUFLOW Event File (TEF) ===
<ol>
<li>Navigate to the '''Module_09\Tutorial_Data''' folder. Copy the '''Event_File.tef''' in to the ''' Module_09\TUFLOW\runs''' folder.
<li>Open the TEF in a text editor. There are three event magnitudes and two event durations listed:
:*Event Magnitudes: 5% AEP, 2% AEP and 1% AEP
:*Event Durations: 1hr and 2hr
<li>The event specific commands are contained between the <tt>Define Event</tt> and <tt>End Define</tt> commands.
<li>The <tt>BC Event Source</tt> command is linked to the bc_dbase file. In the below examples, any occurrence of '~AEP~' in the bc_dbase is replaced with '05p' and any occurrence of '~DUR~' in the bc_dbase is replaced with '1hr'. 
<tt>! EVENT MAGNITUDES</tt> 
<tt>Define Event</tt> <tt> == </tt> <tt>05p</tt>
<ol><tt> BC Event Source </tt> <tt>==</tt> <tt> ~AEP~ | 05p</tt>
</ol><tt>End Define</tt> 
<tt>! EVENT DURATIONS</tt> 
<tt>Define Event</tt> <tt> == </tt> <tt>1hr</tt>
<ol><tt> BC Event Source </tt> <tt>==</tt> <tt> ~DUR~ | 1hr</tt>
</ol><tt>End Define</tt>
Note: The 'AEP' and 'DUR' are user defined variables. The 'AEP' refers to an event magnitude (Annual Exceedance Probability) and 'DUR' refers to an event duration. 
<li>TUFLOW reads all of the defined events in the TEF and selects the ones specified in the batch file. 
</ol>

=== TUFLOW Boundary Condition Database (bc_dbase) ===
Update the bc_dbase with event magnitude and event duration placeholders:
<ol>
<li>Navigate to the '''Module_09\Tutorial_Data''' folder. Copy and paste the following files to the ''' Module_09\TUFLOW\bc_dbase''' folder. These files contains the inflows for each event magnitude and duration.
:*05p1hr.csv
:*05p2hr.csv
:*02p1hr.csv
:*02p2hr.csv
:*01p1hr.csv
:*01p2hr.csv
<li>Save a copy of the '''bc_dbase.csv''' as '''bc_dbase_M09_001.csv'''.
<li>Open the file and make the following updates to the 'Source' column: 
 
[[File:M09 bc dbase 02.png]] 
 
<li>Save the bc_dbase.
</ol>

=== TUFLOW Control File (TCF) ===
<ol>
<li>Save a copy of '''M02_5m_001.tcf''' as '''M09_5m_~e1~_~e2~_001.tcf''' in the '''Module_09\TUFLOW\runs''' folder.
<li>Open the file '''M09_5m_~e1~_~e2~_001.tcf''' in a text editor.
<li>Update the reference to the bc_dbase: 
<tt>BC Database </tt> <tt>== </tt> <tt> ..\bc_dbase\bc_dbase_M09_001.csv </tt> <tt> ! Reference the Boundary Conditions Database</tt> 
<li>Add the following command to reference the TEF at the end of the TCF: 
<tt>! EVENTS</tt> 
<tt>Event File </tt> <tt>== </tt> <tt> Event_File.tef </tt> <tt> ! Reference the TUFLOW Event File</tt> 
<li>Save the TCF.
</ol>
 

= Running the Simulation =
<ol>
<li>Save a copy of '''_run_M02_HPC.bat''' as '''_run_M09_HPC.bat''' in the '''Module_09\TUFLOW\runs''' folder.
<li>Update the batch file to reference the '''M09_5m_~e1~_~e2~_001.tcf''' and include event switches (-e1, -e2) defining event magnitude and event duration: 
<tt>'''set'''</tt> <tt>exe</tt><tt>=</tt><tt>"..\..\..\exe\2025.1.0\TUFLOW_iSP_w64.exe"</tt> 
<tt>'''set'''</tt> <tt>run</tt><tt>=</tt><tt>start "TUFLOW" /wait</tt> <tt> %exe%</tt> <tt> -b</tt> 
<tt>%run% </tt> <tt> -e1 05p -e2 1hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<tt>%run% </tt> <tt> -e1 05p -e2 2hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<tt>%run% </tt> <tt> -e1 02p -e2 1hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<tt>%run% </tt> <tt> -e1 02p -e2 2hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<tt>%run% </tt> <tt> -e1 01p -e2 1hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<tt>%run% </tt> <tt> -e1 01p -e2 2hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<li>Save the batch file and double click it in file explorer to run the simulation. 
</ol>
 

= Troubleshooting =
See tips on common mistakes and troubleshooting steps if the model doesn't run:
:*[[Tutorial_Troubleshooting_QGIS | QGIS]]
 

= Check Files =
While the model is running, review the added features are specified correctly:
<ol>
<li>Open the '''M09_5m_05p_1hr_001.tlf''' from the '''Module_09\TUFLOW\runs\log''' folder in a text editor.
<li>The name of the events replaced the placeholders in the TCF name.
<li>Confirm the specified events and event sources: 
{{Video|name=Animation_M09_Check_01c.mp4|width=928}}
 
</ol>

=Results=
When the model is finished, review the results:
:*[[Tutorial_M09_Results_QGIS | QGIS]]
 

= Conclusion =

* Multiple events were set up in the TUFLOW Event File (TEF) with define event blocks.

*TUFLOW Log Files (TLF) were reviewed.
*Multiple event results were inspected using the TUFLOW Viewer.
*For further training opportunities see [https://tuflow.com/training/training-course-catalogue/ TUFLOW Training Catalogue] and/or contact [mailto:training@tuflow.com training@tuflow.com].
*Alternatively, see the [[TUFLOW_Example_Models#Example_Model_Catalogue | TUFLOW Example Models]] to explore the full list of TUFLOW features.
=Looping Batch File (Optional)=
Looping batch file is a powerful tool running large number of simulations with the least amount of batch file text:
<ol>
<li>Save a copy of '''_run_M09_HPC.bat''' as '''_run_M09_HPC_loop.bat''' in the '''Module_09\TUFLOW\runs''' folder.
<li>Add the following commands. Indentation is important in batch files, insert tabs to match the below spacing: 
<tt>echo </tt> <tt>off</tt> 
<tt>set </tt> <tt>exe</tt><tt>=</tt><tt>"..\..\..\exe\2025.1.0\TUFLOW_iSP_w64.exe"</tt> 
<tt>set </tt> <tt>run</tt><tt>=</tt><tt>start "TUFLOW" /wait </tt><tt>%exe%</tt><tt> -b</tt> 
 
<tt>set</tt> <tt> AEP</tt><tt>=</tt><tt>05p 02p 01p</tt> 
<tt>set</tt> <tt> DUR</tt><tt>=</tt><tt>1hr 2hr</tt> 
 
<tt>for </tt><tt>%%a</tt><tt> in </tt><tt>(</tt><tt>%AEP%</tt><tt>)</tt><tt> do </tt><tt>(</tt> 
<ol><tt> for </tt><tt>%%b</tt><tt> in </tt><tt>(</tt><tt>%DUR%</tt><tt>)</tt><tt> do </tt><tt>( </tt> 
<ol><ol><tt> echo </tt><tt>Running </tt><tt>%%a %%b </tt> 
<tt> %run% </tt><tt>-e1 </tt><tt>%%a</tt><tt> -e2 </tt><tt>%%b </tt><tt>M09_5m_~e1~_~e2~_001.tcf</tt> 
</ol></ol> ) 
</ol>) 
Note: The 'AEP' and 'DUR' are user defined batch file variables. The 'AEP' refers to an event magnitude (Annual Exceedance Probability) and 'DUR' refers to an event duration.

<li>Save the batch file and double click it in file explorer to run the simulation.
</ol>

= Variables (Optional) =
Variables provide a way to define values once and reuse them across control files, which helps manage models with multiple scenarios and events.

Scenarios and events are automatically available as variables. Variables can also be defined using the <tt>Set Variable </tt>command as described in Section 13.2.3 Variables of the TUFLOW manual. They are referenced by enclosing the variable name within << and >>. Example models using variables are provided in the [[TUFLOW Example Models#Bulk Simulation Management|Bulk Simulation Management Example Model Dataset]] on the TUFLOW Wiki.

 

{{Tips Navigation
|uplink=[[Tutorial_Introduction| Back to Tutorial Introduction Main Page]]
}}

Tutorial M09

2026-01-05T04:52:00Z

Abrar.Alttahir: /* Looping Batch File (Optional) */

= Introduction =
In this module, event management is introduced. This is a powerful functionality running multiple event combinations (e.g. magnitude, duration, temporal patterns, climate change) using a single set of control files rather than creating a new set of control files for every simulation. This makes the management of the model easier, ensures consistency between the simulations and better quality control. Without implementing event management, six TCFs and six bc_dbase files are required to run this module, using event management, only one each is needed:

 
[[File:M09 EventManagement 01a.png]] Events are set up using a define event block in TUFLOW Event File (TEF) - <tt>Define Event</tt> command terminated by <tt>End Define</tt>.

Note: If Event logic can be added into most of TUFLOW’s control files where model inputs or parameters may vary between events. The only control files not supporting event logic are the TUFLOW Rainfall Control File (.trcf), TUFLOW External Stress File (.tesf), Advection Control File (.adcf) and within a Define Control command inside a TUFLOW Operational Control (.toc) file.

The TUFLOW control files are:
:*TUFLOW Events File (TEF): A database of TCF and ECF commands for different events. 

'''Module 9 builds from the model created in [[Tutorial_M02 | Module 2]]. The completed Module 2 model is provided in the Module_09\TUFLOW folder.'''

= Simulation Control Files =
=== TUFLOW Event File (TEF) ===
<ol>
<li>Navigate to the '''Module_09\Tutorial_Data''' folder. Copy the '''Event_File.tef''' in to the ''' Module_09\TUFLOW\runs''' folder.
<li>Open the TEF in a text editor. There are three event magnitudes and two event durations listed:
:*Event Magnitudes: 5% AEP, 2% AEP and 1% AEP
:*Event Durations: 1hr and 2hr
<li>The event specific commands are contained between the <tt>Define Event</tt> and <tt>End Define</tt> commands.
<li>The <tt>BC Event Source</tt> command is linked to the bc_dbase file. In the below examples, any occurrence of '~AEP~' in the bc_dbase is replaced with '05p' and any occurrence of '~DUR~' in the bc_dbase is replaced with '1hr'. 
<tt>! EVENT MAGNITUDES</tt> 
<tt>Define Event</tt> <tt> == </tt> <tt>05p</tt>
<ol><tt> BC Event Source </tt> <tt>==</tt> <tt> ~AEP~ | 05p</tt>
</ol><tt>End Define</tt> 
<tt>! EVENT DURATIONS</tt> 
<tt>Define Event</tt> <tt> == </tt> <tt>1hr</tt>
<ol><tt> BC Event Source </tt> <tt>==</tt> <tt> ~DUR~ | 1hr</tt>
</ol><tt>End Define</tt>
Note: The 'AEP' and 'DUR' are user defined variables. The 'AEP' refers to an event magnitude (Annual Exceedance Probability) and 'DUR' refers to an event duration. 
<li>TUFLOW reads all of the defined events in the TEF and selects the ones specified in the batch file. 
</ol>

=== TUFLOW Boundary Condition Database (bc_dbase) ===
Update the bc_dbase with event magnitude and event duration placeholders:
<ol>
<li>Navigate to the '''Module_09\Tutorial_Data''' folder. Copy and paste the following files to the ''' Module_09\TUFLOW\bc_dbase''' folder. These files contains the inflows for each event magnitude and duration.
:*05p1hr.csv
:*05p2hr.csv
:*02p1hr.csv
:*02p2hr.csv
:*01p1hr.csv
:*01p2hr.csv
<li>Save a copy of the '''bc_dbase.csv''' as '''bc_dbase_M09_001.csv'''.
<li>Open the file and make the following updates to the 'Source' column: 
 
[[File:M09 bc dbase 02.png]] 
 
<li>Save the bc_dbase.
</ol>

=== TUFLOW Control File (TCF) ===
<ol>
<li>Save a copy of '''M02_5m_001.tcf''' as '''M09_5m_~e1~_~e2~_001.tcf''' in the '''Module_09\TUFLOW\runs''' folder.
<li>Open the file '''M09_5m_~e1~_~e2~_001.tcf''' in a text editor.
<li>Update the reference to the bc_dbase: 
<tt>BC Database </tt> <tt>== </tt> <tt> ..\bc_dbase\bc_dbase_M09_001.csv </tt> <tt> ! Reference the Boundary Conditions Database</tt> 
<li>Add the following command to reference the TEF at the end of the TCF: 
<tt>! EVENTS</tt> 
<tt>Event File </tt> <tt>== </tt> <tt> Event_File.tef </tt> <tt> ! Reference the TUFLOW Event File</tt> 
<li>Save the TCF.
</ol>
 

= Running the Simulation =
<ol>
<li>Save a copy of '''_run_M02_HPC.bat''' as '''_run_M09_HPC.bat''' in the '''Module_09\TUFLOW\runs''' folder.
<li>Update the batch file to reference the '''M09_5m_~e1~_~e2~_001.tcf''' and include event switches (-e1, -e2) defining event magnitude and event duration: 
<tt>'''set'''</tt> <tt>exe</tt><tt>=</tt><tt>"..\..\..\exe\2025.1.0\TUFLOW_iSP_w64.exe"</tt> 
<tt>'''set'''</tt> <tt>run</tt><tt>=</tt><tt>start "TUFLOW" /wait</tt> <tt> %exe%</tt> <tt> -b</tt> 
<tt>%run% </tt> <tt> -e1 05p -e2 1hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<tt>%run% </tt> <tt> -e1 05p -e2 2hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<tt>%run% </tt> <tt> -e1 02p -e2 1hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<tt>%run% </tt> <tt> -e1 02p -e2 2hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<tt>%run% </tt> <tt> -e1 01p -e2 1hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<tt>%run% </tt> <tt> -e1 01p -e2 2hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<li>Save the batch file and double click it in file explorer to run the simulation. 
</ol>
 

= Troubleshooting =
See tips on common mistakes and troubleshooting steps if the model doesn't run:
:*[[Tutorial_Troubleshooting_QGIS | QGIS]]
 

= Check Files =
While the model is running, review the added features are specified correctly:
<ol>
<li>Open the '''M09_5m_05p_1hr_001.tlf''' from the '''Module_09\TUFLOW\runs\log''' folder in a text editor.
<li>The name of the events replaced the placeholders in the TCF name.
<li>Confirm the specified events and event sources: 
{{Video|name=Animation_M09_Check_01c.mp4|width=928}}
 
</ol>

=Results=
When the model is finished, review the results:
:*[[Tutorial_M09_Results_QGIS | QGIS]]
 

= Conclusion =

* Multiple events were set up in the TUFLOW Event File (TEF) with define event blocks.

*TUFLOW Log Files (TLF) were reviewed.
*Multiple event results were inspected using the TUFLOW Viewer.
*For further training opportunities see [https://tuflow.com/training/training-course-catalogue/ TUFLOW Training Catalogue] and/or contact [mailto:training@tuflow.com training@tuflow.com].
*Alternatively, see the [[TUFLOW_Example_Models#Example_Model_Catalogue | TUFLOW Example Models]] to explore the full list of TUFLOW features.
=Looping Batch File (Optional)=
Looping batch file is a powerful tool running large number of simulations with the least amount of batch file text:
<ol>
<li>Save a copy of '''_run_M09_HPC.bat''' as '''_run_M09_HPC_loop.bat''' in the '''Module_09\TUFLOW\runs''' folder.
<li>Add the following commands. Indentation is important in batch files, insert tabs to match the below spacing: 
<tt>echo </tt> <tt>off</tt> 
<tt>set </tt> <tt>exe</tt><tt>=</tt><tt>"..\..\..\exe\2025.1.0\TUFLOW_iSP_w64.exe"</tt> 
<tt>set </tt> <tt>run</tt><tt>=</tt><tt>start "TUFLOW" /wait </tt><tt>%exe%</tt><tt> -b</tt> 
 
<tt>set</tt> <tt> AEP</tt><tt>=</tt><tt>05p 02p 01p</tt> 
<tt>set</tt> <tt> DUR</tt><tt>=</tt><tt>1hr 2hr</tt> 
 
<tt>for </tt><tt>%%a</tt><tt> in </tt><tt>(</tt><tt>%AEP%</tt><tt>)</tt><tt> do </tt><tt>(</tt> 
<ol><tt> for </tt><tt>%%b</tt><tt> in </tt><tt>(</tt><tt>%DUR%</tt><tt>)</tt><tt> do </tt><tt>( </tt> 
<ol><ol><tt> echo </tt><tt>Running </tt><tt>%%a %%b </tt> 
<tt> %run% </tt><tt>-e1 </tt><tt>%%a</tt><tt> -e2 </tt><tt>%%b </tt><tt>M09_5m_~e1~_~e2~_001.tcf</tt> 
</ol></ol> ) 
</ol>) 
Note: The 'AEP' and 'DUR' are user defined batch file variables. The 'AEP' refers to an event magnitude (Annual Exceedance Probability) and 'DUR' refers to an event duration.

<li>Save the batch file and double click it in file explorer to run the simulation.
</ol>

= Variables (Optional) =
Variables provide a way to define values once and reuse them across control files, which helps manage models with multiple scenarios and events.

Scenarios and events are automatically available as variables. Variables can also be defined using the <tt>Set Variable </tt>command as described in Section 13.2.3 Variables of the TUFLOW manual. They are referenced by enclosing the variable name within << and >>. Example models using variables are provided in the [[TUFLOW Example Models#Bulk Simulation Management|Bulk Simulation Management Example Model Dataset]] on the TUFLOW Wiki. 

{{Tips Navigation
|uplink=[[Tutorial_Introduction| Back to Tutorial Introduction Main Page]]
}}

Tutorial M08

2026-01-05T04:46:48Z

Abrar.Alttahir: /* Variables (Optional) */

= Introduction =
In this module, scenario management is introduced. This is a powerful functionality running different simulations using a single set of control files rather than creating a new set of control files for every simulation. This makes the management of the model easier, ensures consistency between the simulations and better quality control. Without implementing scenario management, six TCFs and six TGC files are required to run this module, using scenario management, only one each is needed: 
 
[[File:M08 ScenarioManagement 02.png]] Scenarios are set up using an if scenario logic block - <tt>If Scenario</tt> command followed by <tt>Else If Scenario</tt> commands adding on different scenarios and terminated by <tt>End If</tt>.

Note: Scenarios can be added to most of TUFLOW’s control files. The only control files that do not support scenarios are the TUFLOW Rainfall Control File (.trcf), TUFLOW External Stress File (.tesf), Advection Control File (.adcf), or within a Define Control command inside a TUFLOW Operational Control (.toc) file.

The tutorial demonstrates two ways of using scenarios:
:*Part 1: Single Scenario - cell size.
:*Part 2: Multiple Scenarios - cell size and topography updates.

The GIS layers are:
:*TGC layers:
<ol><ol><li>2d_zsh: A layer used to modify Zpt elevations using points, lines and polygons.
<li>2d_mat: A layer used to define the land use (material) types within the developmental area.</ol></ol>

'''Module 8 builds from the model created in [[Tutorial_M02 | Module 2]]. The completed Module 2 model is provided in the Module_08\TUFLOW folder.'''

=Part 1 - Single Scenario=
Use scenario management to run the model with a range of different cell sizes, initial timesteps and SGS sample frequency within a single TCF. These steps are often used to conduct a cell size convergence sensitivity testing.

== Simulation Control Files ==
=== TUFLOW Geometry Control File (TGC) ===
<ol>
<li>Save a copy of '''M02_001.tgc''' as '''M08_001.tgc''' in the '''Module_08\TUFLOW\model''' folder. 
<li>Open the '''M08_001.tgc''' in a text editor.
<li>Comment out or delete the 'cell size' command and add the following if scenario logic block: 
<tt>! Cell Size == 5 ! 2D cell size in metres</tt> 
<tt>If Scenario </tt> <tt>== </tt> <tt>10m </tt> <tt> ! Beginning of if statement - 10m scenario</tt> 
<ol><tt>Cell Size </tt> <tt>== </tt> <tt>10 </tt> <tt> ! 2D cell size in metres - 10 metres</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>5m </tt> <tt> ! 5m scenario</tt> 
<ol><tt>Cell Size </tt> <tt>== </tt> <tt>5 </tt> <tt> ! 2D cell size in metres - 5 metres</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>2.5m </tt> <tt> ! 2.5m scenario</tt> 
<ol><tt>Cell Size </tt> <tt>== </tt> <tt>2.5 </tt> <tt> ! 2D cell size in metres - 2.5 metres</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

<li>Save the TGC.
</ol>
Note: The <tt>Pause </tt> command causes TUFLOW to stop every time it encounters it. In the example above, if a cell size was specified other than 10m, 5m, or 2.5m, 'Error: Scenario Name Not Valid' would be displayed. The simulation can then be continued or discontinued via a dialog window.

=== TUFLOW Control File (TCF) ===
<ol>
<li>Save a copy of '''M02_5m_001.tcf''' as '''M08_~s1~_001.tcf''' in the '''Module_08\TUFLOW\runs''' folder.
<li>Open the file '''M08_~s1~_001.tcf''' in a text editor.

<li>Update the TGC reference: 
<tt>Geometry Control File </tt> <tt>== </tt> <tt>..\model\M08_001.tgc </tt> <tt> ! Reference the TUFLOW Geometry Control File</tt> 
<li>The Timestep is dependent on the model cell size. A general guide is to use 1/2 to 1/5 of the model cell size in metres. Comment out or delete the 'Timestep' command and add the following if scenario logic block: 
<tt>! Timestep == 1 ! Specifies the first 2D computational timestep of 1 second</tt> 
<tt>If Scenario </tt> <tt>== </tt> <tt>10m </tt> <tt> ! Beginning of if statement - 10m scenario</tt> 
<ol><tt>Timestep </tt> <tt>== </tt> <tt>2 </tt> <tt> ! Specifies the first 2D computational timestep of 2 seconds</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>5m </tt> <tt> ! 5m scenario</tt> 
<ol><tt>Timestep </tt> <tt>== </tt> <tt>1 </tt> <tt> ! Specifies the first 2D computational timestep of 1 second</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>2.5m </tt> <tt> ! 2.5m scenario</tt> 
<ol><tt>Timestep </tt> <tt>== </tt> <tt>0.5 </tt> <tt> ! Specifies the first 2D computational timestep of 0.5 seconds</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

<li>Save the TCF.
</ol>

== Running the Simulation ==
<ol>
<li>Save a copy of '''_run_M02_HPC.bat''' as '''_run_M08_HPC.bat''' in the '''Module_08\TUFLOW\runs''' folder.
<li>Update the batch file to reference the '''M08_~s1~_001.tcf''' and include a scenario switch (-s1) defining cell size: 
<tt>'''set'''</tt> <tt>exe</tt><tt>=</tt><tt>"..\..\..\exe\2025.1.0\TUFLOW_iSP_w64.exe"</tt> 
<tt>'''set'''</tt> <tt>run</tt><tt>=</tt><tt>start "TUFLOW" /wait</tt> <tt> %exe%</tt> <tt> -b</tt> 
<tt>%run% </tt> <tt> -s1 10m M08_~s1~_001.tcf </tt> 
<tt>%run% </tt> <tt> -s1 5m M08_~s1~_001.tcf </tt> 
<tt>%run% </tt> <tt> -s1 2.5m M08_~s1~_001.tcf </tt> 
<li>Save the batch file and double click it in file explorer to run the simulation.
</ol>

== Troubleshooting ==
See tips on common mistakes and troubleshooting steps if the model doesn't run:
:*[[Tutorial_Troubleshooting_QGIS | QGIS]]

== Check Files ==
While the model is running, review the added features are specified correctly:
:*[[Tutorial_M08_001_Check_Files_QGIS | QGIS - SHP]]
:*[[Tutorial_M08_001_Check_Files_QGIS_GPKG | QGIS - GPKG]]
Compare the model cell count:
<ol>
<li>Navigate to the '''Module_08\TUFLOW\runs\log''' folder and open the following files in a text editor:
:*'''M08_10m_001.tlf'''
:*'''M08_5m_001.tlf'''
:*'''M08_2.5m_001.tlf'''
<li>Search for 'Number 2D Cells' and view the number of active cells for each model. This is the number of active cells as specified by the 2d_code layer. There are approximately 16 times more cells in the 2.5m model compared to the 10m model:
:*M08_10m_001: 5122
:*M08_5m_001: 20486
:*M08_2.5m_001: 81869
</ol>

== Results==
When the model is finished, review the results:
:*[[Tutorial_M08_001_Results_QGIS | QGIS]]
Compare the model run times:
<ol>
<li>Navigate to the '''Module_08\TUFLOW\runs''' folder and open the '''_TUFLOW Simulations.log''' in a text editor.
<li>The file shows the simulation times for all the simulations.
 
[[File:M08_TUFLOW_Simulations_f.png]] 
 
</ol>
 

=Part 2 - Multiple Scenarios=
Use scenario management to run the model with a range of different cell sizes and topography changes within a single TCF. These steps are often used to conduct an impact assessment.
 

== GIS Inputs ==
Create, import and view input data: 
:*[[Tutorial_M08_002_GIS_Inputs_QGIS | QGIS - SHP]] 
:*[[Tutorial_M08_002_GIS_Inputs_QGIS_GPKG | QGIS - GPKG]] 

== Simulation Control Files ==
=== TUFLOW Geometry Control File (TGC) ===
<ol>
<li>Save a copy of '''M08_001.tgc''' as '''M08_002.tgc''' in the '''Module_08\TUFLOW\model''' folder. 
<li>Open the '''M08_002.tgc''' in a text editor.
<li>Include the following if scenario logic block for existing and developed case after the '<tt>Read GIS Z Shape</tt>' command: 

'''QGIS - SHP''' 
<tt>If Scenario </tt> <tt>== </tt> <tt>EXG </tt> <tt> ! Beginning of if statement - existing scenario</tt> 
<ol><tt> ! Defines a placeholder</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>DEV </tt> <tt> ! Development scenario</tt> 
<ol><tt>Read GIS Z Shape </tt> <tt>== </tt> <tt>gis\2d_zsh_M02_landscape_002_R.shp | gis\2d_zsh_M02_landscape_002_L.shp | gis\2d_zsh_M02_landscape_002_P.shp </tt> <tt> ! Defines areas of complex landscaping</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

'''QGIS - GPKG''' 
<tt>If Scenario </tt> <tt>== </tt> <tt>EXG </tt> <tt> ! Beginning of if statement - existing scenario</tt> 
<ol><tt> ! Defines a placeholder</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>DEV </tt> <tt> ! Development scenario</tt> 
<ol><tt>Read GIS Z Shape </tt> <tt>== </tt> <tt>2d_zsh_M02_landscape_002_R | 2d_zsh_M02_landscape_002_L | 2d_zsh_M02_landscape_002_P </tt> <tt> ! Defines areas of complex landscaping</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

<li>Include the following if scenario logic block for existing and developed case after the '<tt>Read GIS Mat</tt>' command: 

'''QGIS - SHP''' 
<tt>If Scenario </tt> <tt>== </tt> <tt>EXG </tt> <tt> ! Beginning of if statement - existing scenario</tt> 
<ol><tt> ! Defines a placeholder</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>DEV </tt> <tt> ! Development scenario</tt> 
<ol><tt>Read GIS Mat </tt> <tt>== </tt> <tt>gis\2d_mat_M02_landscape_002_R.shp </tt> <tt> ! Sets material values according to attributes in the GIS layer</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

'''QGIS - GPKG''' 
<tt>If Scenario </tt> <tt>== </tt> <tt>EXG </tt> <tt> ! Beginning of if statement - existing scenario</tt> 
<ol><tt> ! Defines a placeholder</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>DEV </tt> <tt> ! Development scenario</tt> 
<ol><tt>Read GIS Mat </tt> <tt>== </tt> <tt>2d_mat_M02_landscape_002_R </tt> <tt> ! Sets material values according to attributes in the GIS layer</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

<li>Save the TGC.
</ol>

=== TUFLOW Control File (TCF) ===
<ol>
<li>Save a copy of '''M08_~s1~_001.tcf''' as '''M08_~s1~_~s2~_002.tcf''' in the '''Module_08\TUFLOW\runs''' folder.
<li>Open the file '''M08_~s1~_~s2~_002.tcf''' in a text editor.
<li>Update the TGC reference: 
<tt>Geometry Control File </tt> <tt>== </tt> <tt>..\model\M08_002.tgc </tt> <tt> ! Reference the TUFLOW Geometry Control File</tt> 
<li>For GeoPackage users, update the Spatial Database reference: 
'''QGIS - GPKG''' 
<tt>Spatial Database </tt> <tt>== </tt> <tt>..\model\gis\M08_002.gpkg</tt> <tt> ! Specify the location of the GeoPackage Spatial Database</tt> 
<li>Save the TCF.
</ol>

== Running the Simulation ==
<ol>
<li>Open '''_run_M08_HPC.bat''' from the '''Module_08\TUFLOW\runs''' folder.
<li>Update the batch file to reference the '''M08_~s1~_~s2~_002.tcf''' and in addition to cell size scenario switch (-s1) include second scenario switch (-s2) defining topography: 
<tt>'''set'''</tt> <tt>exe</tt><tt>=</tt><tt>"..\..\..\exe\2025.1.0\TUFLOW_iSP_w64.exe"</tt> 
<tt>'''set'''</tt> <tt>run</tt><tt>=</tt><tt>start "TUFLOW" /wait</tt> <tt> %exe%</tt> <tt> -b</tt> 
<tt>%run% </tt> <tt> -s1 2.5m -s2 EXG M08_~s1~_~s2~_002.tcf </tt> 
<tt>%run% </tt> <tt> -s1 2.5m -s2 DEV M08_~s1~_~s2~_002.tcf </tt> 
<tt>%run% </tt> <tt> -s1 5m -s2 EXG M08_~s1~_~s2~_002.tcf </tt> 
<tt>%run% </tt> <tt> -s1 5m -s2 DEV M08_~s1~_~s2~_002.tcf </tt> 
<tt>%run% </tt> <tt> -s1 10m -s2 EXG M08_~s1~_~s2~_002.tcf </tt> 
<tt>%run% </tt> <tt> -s1 10m -s2 DEV M08_~s1~_~s2~_002.tcf </tt> 
<li>Save the batch file and double click it in file explorer to run the simulation. 
</ol>

== Troubleshooting ==
See tips on common mistakes and troubleshooting steps if the model doesn't run:
:*[[Tutorial_Troubleshooting_QGIS | QGIS]]

== Check Files ==
While the model is running, review the added features are specified correctly:
:*[[Tutorial_M08_002_Check_Files_QGIS | QGIS]]

== Results==
When the model is finished, review the results:
:*[[Tutorial_M08_002_Results_QGIS | QGIS]]
 

= Conclusion =
:*Single and multiple scenarios were set up and run with a range of cell sizes and designs.
:*Check files were assessed to view the changes based on each scenario.
:*Multiple scenario results were viewed using the TUFLOW Viewer.
:*For further training opportunities see [https://tuflow.com/training/training-course-catalogue/ TUFLOW Training Catalogue] and/or contact [mailto:training@tuflow.com training@tuflow.com].
:*Alternatively, see the [[TUFLOW_Example_Models#Example_Model_Catalogue | TUFLOW Example Models]] to explore the full list of TUFLOW features.
=Looping Batch File (Optional)=
Looping batch file is a powerful tool running large number of simulations with the least amount of batch file text:
<ol>
<li>Save a copy of '''_run_M08_HPC.bat''' as '''_run_M08_HPC_loop.bat''' in the '''Module_08\TUFLOW\runs''' folder.
<li>Add the following commands. Indentation is important in batch files, insert tabs to match the below spacing: 
<tt>echo </tt> <tt>off</tt> 
<tt>set </tt> <tt>exe</tt><tt>=</tt><tt>"..\..\..\exe\2025.1.0\TUFLOW_iSP_w64.exe"</tt> 
<tt>set </tt> <tt>run</tt><tt>=</tt><tt>start "TUFLOW" /wait </tt><tt>%exe%</tt><tt> -b</tt> 
 
<tt>set</tt> <tt> CSZ</tt><tt>=</tt><tt>10m 5m 2.5m</tt> 
<tt>set</tt> <tt> DES</tt><tt>=</tt><tt>EXG DEV</tt> 
 
<tt>for </tt><tt>%%a</tt><tt> in </tt><tt>(</tt><tt>%CSZ%</tt><tt>)</tt><tt> do </tt><tt>(</tt> 
<ol><tt> for </tt><tt>%%b</tt><tt> in </tt><tt>(</tt><tt>%DES%</tt><tt>)</tt><tt> do </tt><tt>( </tt> 
<ol><ol><tt> echo </tt><tt>Running </tt><tt>%%a %%b </tt> 
<tt> %run% </tt><tt>-s1 </tt><tt>%%a</tt><tt> -s2 </tt><tt>%%b </tt><tt>M08_~s1~_~s2~_002.tcf</tt> 
</ol></ol> ) 
</ol>) 
Note: The 'CSZ' and 'DES' are user defined batch file variables. The 'CSZ' refers to a cell size and 'DES' refers to a design scenario. 

<li>Save the batch file and double click it in file explorer to run the simulation.
</ol>

= Variables (Optional) =
Variables provide a way to define values once and reuse them across control files, which helps manage models with multiple scenarios and events.

Scenarios and events are automatically available as variables. Variables can also be defined using the <tt>Set Variable </tt>command as described in Section 13.2.3 Variables of the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW manual]. They are referenced by enclosing the variable name within << and >>. Example models using variables are provided in the [[TUFLOW Example Models#Bulk Simulation Management|Bulk Simulation Management Example Model Dataset]] on the TUFLOW Wiki.

 

{{Tips Navigation
|uplink=[[Tutorial_Introduction| Back to Tutorial Introduction Main Page]]
}}

Tutorial M08

2026-01-05T03:55:10Z

Abrar.Alttahir: /* Looping Batch File (Optional) */

= Introduction =
In this module, scenario management is introduced. This is a powerful functionality running different simulations using a single set of control files rather than creating a new set of control files for every simulation. This makes the management of the model easier, ensures consistency between the simulations and better quality control. Without implementing scenario management, six TCFs and six TGC files are required to run this module, using scenario management, only one each is needed: 
 
[[File:M08 ScenarioManagement 02.png]] Scenarios are set up using an if scenario logic block - <tt>If Scenario</tt> command followed by <tt>Else If Scenario</tt> commands adding on different scenarios and terminated by <tt>End If</tt>.

Note: Scenarios can be added to most of TUFLOW’s control files. The only control files that do not support scenarios are the TUFLOW Rainfall Control File (.trcf), TUFLOW External Stress File (.tesf), Advection Control File (.adcf), or within a Define Control command inside a TUFLOW Operational Control (.toc) file.

The tutorial demonstrates two ways of using scenarios:
:*Part 1: Single Scenario - cell size.
:*Part 2: Multiple Scenarios - cell size and topography updates.

The GIS layers are:
:*TGC layers:
<ol><ol><li>2d_zsh: A layer used to modify Zpt elevations using points, lines and polygons.
<li>2d_mat: A layer used to define the land use (material) types within the developmental area.</ol></ol>

'''Module 8 builds from the model created in [[Tutorial_M02 | Module 2]]. The completed Module 2 model is provided in the Module_08\TUFLOW folder.'''

=Part 1 - Single Scenario=
Use scenario management to run the model with a range of different cell sizes, initial timesteps and SGS sample frequency within a single TCF. These steps are often used to conduct a cell size convergence sensitivity testing.

== Simulation Control Files ==
=== TUFLOW Geometry Control File (TGC) ===
<ol>
<li>Save a copy of '''M02_001.tgc''' as '''M08_001.tgc''' in the '''Module_08\TUFLOW\model''' folder. 
<li>Open the '''M08_001.tgc''' in a text editor.
<li>Comment out or delete the 'cell size' command and add the following if scenario logic block: 
<tt>! Cell Size == 5 ! 2D cell size in metres</tt> 
<tt>If Scenario </tt> <tt>== </tt> <tt>10m </tt> <tt> ! Beginning of if statement - 10m scenario</tt> 
<ol><tt>Cell Size </tt> <tt>== </tt> <tt>10 </tt> <tt> ! 2D cell size in metres - 10 metres</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>5m </tt> <tt> ! 5m scenario</tt> 
<ol><tt>Cell Size </tt> <tt>== </tt> <tt>5 </tt> <tt> ! 2D cell size in metres - 5 metres</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>2.5m </tt> <tt> ! 2.5m scenario</tt> 
<ol><tt>Cell Size </tt> <tt>== </tt> <tt>2.5 </tt> <tt> ! 2D cell size in metres - 2.5 metres</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

<li>Save the TGC.
</ol>
Note: The <tt>Pause </tt> command causes TUFLOW to stop every time it encounters it. In the example above, if a cell size was specified other than 10m, 5m, or 2.5m, 'Error: Scenario Name Not Valid' would be displayed. The simulation can then be continued or discontinued via a dialog window.

=== TUFLOW Control File (TCF) ===
<ol>
<li>Save a copy of '''M02_5m_001.tcf''' as '''M08_~s1~_001.tcf''' in the '''Module_08\TUFLOW\runs''' folder.
<li>Open the file '''M08_~s1~_001.tcf''' in a text editor.

<li>Update the TGC reference: 
<tt>Geometry Control File </tt> <tt>== </tt> <tt>..\model\M08_001.tgc </tt> <tt> ! Reference the TUFLOW Geometry Control File</tt> 
<li>The Timestep is dependent on the model cell size. A general guide is to use 1/2 to 1/5 of the model cell size in metres. Comment out or delete the 'Timestep' command and add the following if scenario logic block: 
<tt>! Timestep == 1 ! Specifies the first 2D computational timestep of 1 second</tt> 
<tt>If Scenario </tt> <tt>== </tt> <tt>10m </tt> <tt> ! Beginning of if statement - 10m scenario</tt> 
<ol><tt>Timestep </tt> <tt>== </tt> <tt>2 </tt> <tt> ! Specifies the first 2D computational timestep of 2 seconds</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>5m </tt> <tt> ! 5m scenario</tt> 
<ol><tt>Timestep </tt> <tt>== </tt> <tt>1 </tt> <tt> ! Specifies the first 2D computational timestep of 1 second</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>2.5m </tt> <tt> ! 2.5m scenario</tt> 
<ol><tt>Timestep </tt> <tt>== </tt> <tt>0.5 </tt> <tt> ! Specifies the first 2D computational timestep of 0.5 seconds</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

<li>Save the TCF.
</ol>

== Running the Simulation ==
<ol>
<li>Save a copy of '''_run_M02_HPC.bat''' as '''_run_M08_HPC.bat''' in the '''Module_08\TUFLOW\runs''' folder.
<li>Update the batch file to reference the '''M08_~s1~_001.tcf''' and include a scenario switch (-s1) defining cell size: 
<tt>'''set'''</tt> <tt>exe</tt><tt>=</tt><tt>"..\..\..\exe\2025.1.0\TUFLOW_iSP_w64.exe"</tt> 
<tt>'''set'''</tt> <tt>run</tt><tt>=</tt><tt>start "TUFLOW" /wait</tt> <tt> %exe%</tt> <tt> -b</tt> 
<tt>%run% </tt> <tt> -s1 10m M08_~s1~_001.tcf </tt> 
<tt>%run% </tt> <tt> -s1 5m M08_~s1~_001.tcf </tt> 
<tt>%run% </tt> <tt> -s1 2.5m M08_~s1~_001.tcf </tt> 
<li>Save the batch file and double click it in file explorer to run the simulation.
</ol>

== Troubleshooting ==
See tips on common mistakes and troubleshooting steps if the model doesn't run:
:*[[Tutorial_Troubleshooting_QGIS | QGIS]]

== Check Files ==
While the model is running, review the added features are specified correctly:
:*[[Tutorial_M08_001_Check_Files_QGIS | QGIS - SHP]]
:*[[Tutorial_M08_001_Check_Files_QGIS_GPKG | QGIS - GPKG]]
Compare the model cell count:
<ol>
<li>Navigate to the '''Module_08\TUFLOW\runs\log''' folder and open the following files in a text editor:
:*'''M08_10m_001.tlf'''
:*'''M08_5m_001.tlf'''
:*'''M08_2.5m_001.tlf'''
<li>Search for 'Number 2D Cells' and view the number of active cells for each model. This is the number of active cells as specified by the 2d_code layer. There are approximately 16 times more cells in the 2.5m model compared to the 10m model:
:*M08_10m_001: 5122
:*M08_5m_001: 20486
:*M08_2.5m_001: 81869
</ol>

== Results==
When the model is finished, review the results:
:*[[Tutorial_M08_001_Results_QGIS | QGIS]]
Compare the model run times:
<ol>
<li>Navigate to the '''Module_08\TUFLOW\runs''' folder and open the '''_TUFLOW Simulations.log''' in a text editor.
<li>The file shows the simulation times for all the simulations.
 
[[File:M08_TUFLOW_Simulations_f.png]] 
 
</ol>
 

=Part 2 - Multiple Scenarios=
Use scenario management to run the model with a range of different cell sizes and topography changes within a single TCF. These steps are often used to conduct an impact assessment.
 

== GIS Inputs ==
Create, import and view input data: 
:*[[Tutorial_M08_002_GIS_Inputs_QGIS | QGIS - SHP]] 
:*[[Tutorial_M08_002_GIS_Inputs_QGIS_GPKG | QGIS - GPKG]] 

== Simulation Control Files ==
=== TUFLOW Geometry Control File (TGC) ===
<ol>
<li>Save a copy of '''M08_001.tgc''' as '''M08_002.tgc''' in the '''Module_08\TUFLOW\model''' folder. 
<li>Open the '''M08_002.tgc''' in a text editor.
<li>Include the following if scenario logic block for existing and developed case after the '<tt>Read GIS Z Shape</tt>' command: 

'''QGIS - SHP''' 
<tt>If Scenario </tt> <tt>== </tt> <tt>EXG </tt> <tt> ! Beginning of if statement - existing scenario</tt> 
<ol><tt> ! Defines a placeholder</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>DEV </tt> <tt> ! Development scenario</tt> 
<ol><tt>Read GIS Z Shape </tt> <tt>== </tt> <tt>gis\2d_zsh_M02_landscape_002_R.shp | gis\2d_zsh_M02_landscape_002_L.shp | gis\2d_zsh_M02_landscape_002_P.shp </tt> <tt> ! Defines areas of complex landscaping</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

'''QGIS - GPKG''' 
<tt>If Scenario </tt> <tt>== </tt> <tt>EXG </tt> <tt> ! Beginning of if statement - existing scenario</tt> 
<ol><tt> ! Defines a placeholder</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>DEV </tt> <tt> ! Development scenario</tt> 
<ol><tt>Read GIS Z Shape </tt> <tt>== </tt> <tt>2d_zsh_M02_landscape_002_R | 2d_zsh_M02_landscape_002_L | 2d_zsh_M02_landscape_002_P </tt> <tt> ! Defines areas of complex landscaping</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

<li>Include the following if scenario logic block for existing and developed case after the '<tt>Read GIS Mat</tt>' command: 

'''QGIS - SHP''' 
<tt>If Scenario </tt> <tt>== </tt> <tt>EXG </tt> <tt> ! Beginning of if statement - existing scenario</tt> 
<ol><tt> ! Defines a placeholder</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>DEV </tt> <tt> ! Development scenario</tt> 
<ol><tt>Read GIS Mat </tt> <tt>== </tt> <tt>gis\2d_mat_M02_landscape_002_R.shp </tt> <tt> ! Sets material values according to attributes in the GIS layer</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

'''QGIS - GPKG''' 
<tt>If Scenario </tt> <tt>== </tt> <tt>EXG </tt> <tt> ! Beginning of if statement - existing scenario</tt> 
<ol><tt> ! Defines a placeholder</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>DEV </tt> <tt> ! Development scenario</tt> 
<ol><tt>Read GIS Mat </tt> <tt>== </tt> <tt>2d_mat_M02_landscape_002_R </tt> <tt> ! Sets material values according to attributes in the GIS layer</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

<li>Save the TGC.
</ol>

=== TUFLOW Control File (TCF) ===
<ol>
<li>Save a copy of '''M08_~s1~_001.tcf''' as '''M08_~s1~_~s2~_002.tcf''' in the '''Module_08\TUFLOW\runs''' folder.
<li>Open the file '''M08_~s1~_~s2~_002.tcf''' in a text editor.
<li>Update the TGC reference: 
<tt>Geometry Control File </tt> <tt>== </tt> <tt>..\model\M08_002.tgc </tt> <tt> ! Reference the TUFLOW Geometry Control File</tt> 
<li>For GeoPackage users, update the Spatial Database reference: 
'''QGIS - GPKG''' 
<tt>Spatial Database </tt> <tt>== </tt> <tt>..\model\gis\M08_002.gpkg</tt> <tt> ! Specify the location of the GeoPackage Spatial Database</tt> 
<li>Save the TCF.
</ol>

== Running the Simulation ==
<ol>
<li>Open '''_run_M08_HPC.bat''' from the '''Module_08\TUFLOW\runs''' folder.
<li>Update the batch file to reference the '''M08_~s1~_~s2~_002.tcf''' and in addition to cell size scenario switch (-s1) include second scenario switch (-s2) defining topography: 
<tt>'''set'''</tt> <tt>exe</tt><tt>=</tt><tt>"..\..\..\exe\2025.1.0\TUFLOW_iSP_w64.exe"</tt> 
<tt>'''set'''</tt> <tt>run</tt><tt>=</tt><tt>start "TUFLOW" /wait</tt> <tt> %exe%</tt> <tt> -b</tt> 
<tt>%run% </tt> <tt> -s1 2.5m -s2 EXG M08_~s1~_~s2~_002.tcf </tt> 
<tt>%run% </tt> <tt> -s1 2.5m -s2 DEV M08_~s1~_~s2~_002.tcf </tt> 
<tt>%run% </tt> <tt> -s1 5m -s2 EXG M08_~s1~_~s2~_002.tcf </tt> 
<tt>%run% </tt> <tt> -s1 5m -s2 DEV M08_~s1~_~s2~_002.tcf </tt> 
<tt>%run% </tt> <tt> -s1 10m -s2 EXG M08_~s1~_~s2~_002.tcf </tt> 
<tt>%run% </tt> <tt> -s1 10m -s2 DEV M08_~s1~_~s2~_002.tcf </tt> 
<li>Save the batch file and double click it in file explorer to run the simulation. 
</ol>

== Troubleshooting ==
See tips on common mistakes and troubleshooting steps if the model doesn't run:
:*[[Tutorial_Troubleshooting_QGIS | QGIS]]

== Check Files ==
While the model is running, review the added features are specified correctly:
:*[[Tutorial_M08_002_Check_Files_QGIS | QGIS]]

== Results==
When the model is finished, review the results:
:*[[Tutorial_M08_002_Results_QGIS | QGIS]]
 

= Conclusion =
:*Single and multiple scenarios were set up and run with a range of cell sizes and designs.
:*Check files were assessed to view the changes based on each scenario.
:*Multiple scenario results were viewed using the TUFLOW Viewer.
:*For further training opportunities see [https://tuflow.com/training/training-course-catalogue/ TUFLOW Training Catalogue] and/or contact [mailto:training@tuflow.com training@tuflow.com].
:*Alternatively, see the [[TUFLOW_Example_Models#Example_Model_Catalogue | TUFLOW Example Models]] to explore the full list of TUFLOW features.
=Looping Batch File (Optional)=
Looping batch file is a powerful tool running large number of simulations with the least amount of batch file text:
<ol>
<li>Save a copy of '''_run_M08_HPC.bat''' as '''_run_M08_HPC_loop.bat''' in the '''Module_08\TUFLOW\runs''' folder.
<li>Add the following commands. Indentation is important in batch files, insert tabs to match the below spacing: 
<tt>echo </tt> <tt>off</tt> 
<tt>set </tt> <tt>exe</tt><tt>=</tt><tt>"..\..\..\exe\2025.1.0\TUFLOW_iSP_w64.exe"</tt> 
<tt>set </tt> <tt>run</tt><tt>=</tt><tt>start "TUFLOW" /wait </tt><tt>%exe%</tt><tt> -b</tt> 
 
<tt>set</tt> <tt> CSZ</tt><tt>=</tt><tt>10m 5m 2.5m</tt> 
<tt>set</tt> <tt> DES</tt><tt>=</tt><tt>EXG DEV</tt> 
 
<tt>for </tt><tt>%%a</tt><tt> in </tt><tt>(</tt><tt>%CSZ%</tt><tt>)</tt><tt> do </tt><tt>(</tt> 
<ol><tt> for </tt><tt>%%b</tt><tt> in </tt><tt>(</tt><tt>%DES%</tt><tt>)</tt><tt> do </tt><tt>( </tt> 
<ol><ol><tt> echo </tt><tt>Running </tt><tt>%%a %%b </tt> 
<tt> %run% </tt><tt>-s1 </tt><tt>%%a</tt><tt> -s2 </tt><tt>%%b </tt><tt>M08_~s1~_~s2~_002.tcf</tt> 
</ol></ol> ) 
</ol>) 
Note: The 'CSZ' and 'DES' are user defined batch file variables. The 'CSZ' refers to a cell size and 'DES' refers to a design scenario. 

<li>Save the batch file and double click it in file explorer to run the simulation.
</ol>

= Variables (Optional) =
Variables provide a way to define values once and reuse them across control files, which helps manage models with multiple scenarios and events.

Scenarios and events are automatically available as variables. Variables can also be defined using the <tt>Set Variable </tt>command as described in Section 13.2.3 Variables of the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW manual]. They are referenced by enclosing the variable name within << and >>. Example models using variables are provided in the [[TUFLOW Example Models#Bulk Simulation Management|Bulk Simulation Management Example Model Dataset]] on the TUFLOW Wiki. 

{{Tips Navigation
|uplink=[[Tutorial_Introduction| Back to Tutorial Introduction Main Page]]
}}

Tutorial M09

2026-01-05T02:50:25Z

Abrar.Alttahir: /* Introduction */

= Introduction =
In this module, event management is introduced. This is a powerful functionality running multiple event combinations (e.g. magnitude, duration, temporal patterns, climate change) using a single set of control files rather than creating a new set of control files for every simulation. This makes the management of the model easier, ensures consistency between the simulations and better quality control. Without implementing event management, six TCFs and six bc_dbase files are required to run this module, using event management, only one each is needed:

 
[[File:M09 EventManagement 01a.png]] Events are set up using a define event block in TUFLOW Event File (TEF) - <tt>Define Event</tt> command terminated by <tt>End Define</tt>.

Note: If Event logic can be added into most of TUFLOW’s control files where model inputs or parameters may vary between events. The only control files not supporting event logic are the TUFLOW Rainfall Control File (.trcf), TUFLOW External Stress File (.tesf), Advection Control File (.adcf) and within a Define Control command inside a TUFLOW Operational Control (.toc) file.

The TUFLOW control files are:
:*TUFLOW Events File (TEF): A database of TCF and ECF commands for different events. 

'''Module 9 builds from the model created in [[Tutorial_M02 | Module 2]]. The completed Module 2 model is provided in the Module_09\TUFLOW folder.'''

= Simulation Control Files =
=== TUFLOW Event File (TEF) ===
<ol>
<li>Navigate to the '''Module_09\Tutorial_Data''' folder. Copy the '''Event_File.tef''' in to the ''' Module_09\TUFLOW\runs''' folder.
<li>Open the TEF in a text editor. There are three event magnitudes and two event durations listed:
:*Event Magnitudes: 5% AEP, 2% AEP and 1% AEP
:*Event Durations: 1hr and 2hr
<li>The event specific commands are contained between the <tt>Define Event</tt> and <tt>End Define</tt> commands.
<li>The <tt>BC Event Source</tt> command is linked to the bc_dbase file. In the below examples, any occurrence of '~AEP~' in the bc_dbase is replaced with '05p' and any occurrence of '~DUR~' in the bc_dbase is replaced with '1hr'. 
<tt>! EVENT MAGNITUDES</tt> 
<tt>Define Event</tt> <tt> == </tt> <tt>05p</tt>
<ol><tt> BC Event Source </tt> <tt>==</tt> <tt> ~AEP~ | 05p</tt>
</ol><tt>End Define</tt> 
<tt>! EVENT DURATIONS</tt> 
<tt>Define Event</tt> <tt> == </tt> <tt>1hr</tt>
<ol><tt> BC Event Source </tt> <tt>==</tt> <tt> ~DUR~ | 1hr</tt>
</ol><tt>End Define</tt>
Note: The 'AEP' and 'DUR' are user defined variables. The 'AEP' refers to an event magnitude (Annual Exceedance Probability) and 'DUR' refers to an event duration. 
<li>TUFLOW reads all of the defined events in the TEF and selects the ones specified in the batch file. 
</ol>

=== TUFLOW Boundary Condition Database (bc_dbase) ===
Update the bc_dbase with event magnitude and event duration placeholders:
<ol>
<li>Navigate to the '''Module_09\Tutorial_Data''' folder. Copy and paste the following files to the ''' Module_09\TUFLOW\bc_dbase''' folder. These files contains the inflows for each event magnitude and duration.
:*05p1hr.csv
:*05p2hr.csv
:*02p1hr.csv
:*02p2hr.csv
:*01p1hr.csv
:*01p2hr.csv
<li>Save a copy of the '''bc_dbase.csv''' as '''bc_dbase_M09_001.csv'''.
<li>Open the file and make the following updates to the 'Source' column: 
 
[[File:M09 bc dbase 02.png]] 
 
<li>Save the bc_dbase.
</ol>

=== TUFLOW Control File (TCF) ===
<ol>
<li>Save a copy of '''M02_5m_001.tcf''' as '''M09_5m_~e1~_~e2~_001.tcf''' in the '''Module_09\TUFLOW\runs''' folder.
<li>Open the file '''M09_5m_~e1~_~e2~_001.tcf''' in a text editor.
<li>Update the reference to the bc_dbase: 
<tt>BC Database </tt> <tt>== </tt> <tt> ..\bc_dbase\bc_dbase_M09_001.csv </tt> <tt> ! Reference the Boundary Conditions Database</tt> 
<li>Add the following command to reference the TEF at the end of the TCF: 
<tt>! EVENTS</tt> 
<tt>Event File </tt> <tt>== </tt> <tt> Event_File.tef </tt> <tt> ! Reference the TUFLOW Event File</tt> 
<li>Save the TCF.
</ol>
 

= Running the Simulation =
<ol>
<li>Save a copy of '''_run_M02_HPC.bat''' as '''_run_M09_HPC.bat''' in the '''Module_09\TUFLOW\runs''' folder.
<li>Update the batch file to reference the '''M09_5m_~e1~_~e2~_001.tcf''' and include event switches (-e1, -e2) defining event magnitude and event duration: 
<tt>'''set'''</tt> <tt>exe</tt><tt>=</tt><tt>"..\..\..\exe\2025.1.0\TUFLOW_iSP_w64.exe"</tt> 
<tt>'''set'''</tt> <tt>run</tt><tt>=</tt><tt>start "TUFLOW" /wait</tt> <tt> %exe%</tt> <tt> -b</tt> 
<tt>%run% </tt> <tt> -e1 05p -e2 1hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<tt>%run% </tt> <tt> -e1 05p -e2 2hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<tt>%run% </tt> <tt> -e1 02p -e2 1hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<tt>%run% </tt> <tt> -e1 02p -e2 2hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<tt>%run% </tt> <tt> -e1 01p -e2 1hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<tt>%run% </tt> <tt> -e1 01p -e2 2hr M09_5m_~e1~_~e2~_001.tcf </tt> 
<li>Save the batch file and double click it in file explorer to run the simulation. 
</ol>
 

= Troubleshooting =
See tips on common mistakes and troubleshooting steps if the model doesn't run:
:*[[Tutorial_Troubleshooting_QGIS | QGIS]]
 

= Check Files =
While the model is running, review the added features are specified correctly:
<ol>
<li>Open the '''M09_5m_05p_1hr_001.tlf''' from the '''Module_09\TUFLOW\runs\log''' folder in a text editor.
<li>The name of the events replaced the placeholders in the TCF name.
<li>Confirm the specified events and event sources: 
{{Video|name=Animation_M09_Check_01c.mp4|width=928}}
 
</ol>

=Results=
When the model is finished, review the results:
:*[[Tutorial_M09_Results_QGIS | QGIS]]
 

= Conclusion =
:*Multiple events were set up in the TUFLOW Event File (TEF) with define event blocks.
:*TUFLOW Log Files (TLF) were reviewed.
:*Multiple event results were inspected using the TUFLOW Viewer.
:*For further training opportunities see [https://tuflow.com/training/training-course-catalogue/ TUFLOW Training Catalogue] and/or contact [mailto:training@tuflow.com training@tuflow.com]. 
:*Alternatively, see the [[TUFLOW_Example_Models#Example_Model_Catalogue | TUFLOW Example Models]] to explore the full list of TUFLOW features.
 

=Looping Batch File (Optional)=
Looping batch file is a powerful tool running large number of simulations with the least amount of batch file text:
<ol>
<li>Save a copy of '''_run_M09_HPC.bat''' as '''_run_M09_HPC_loop.bat''' in the '''Module_09\TUFLOW\runs''' folder.
<li>Add the following commands. Indentation is important in batch files, insert tabs to match the below spacing: 
<tt>echo </tt> <tt>off</tt> 
<tt>set </tt> <tt>exe</tt><tt>=</tt><tt>"..\..\..\exe\2025.1.0\TUFLOW_iSP_w64.exe"</tt> 
<tt>set </tt> <tt>run</tt><tt>=</tt><tt>start "TUFLOW" /wait </tt><tt>%exe%</tt><tt> -b</tt> 
 
<tt>set</tt> <tt> AEP</tt><tt>=</tt><tt>05p 02p 01p</tt> 
<tt>set</tt> <tt> DUR</tt><tt>=</tt><tt>1hr 2hr</tt> 
 
<tt>for </tt><tt>%%a</tt><tt> in </tt><tt>(</tt><tt>%AEP%</tt><tt>)</tt><tt> do </tt><tt>(</tt> 
<ol><tt> for </tt><tt>%%b</tt><tt> in </tt><tt>(</tt><tt>%DUR%</tt><tt>)</tt><tt> do </tt><tt>( </tt> 
<ol><ol><tt> echo </tt><tt>Running </tt><tt>%%a %%b </tt> 
<tt> %run% </tt><tt>-e1 </tt><tt>%%a</tt><tt> -e2 </tt><tt>%%b </tt><tt>M09_5m_~e1~_~e2~_001.tcf</tt> 
</ol></ol> ) 
</ol>) 
Note: The 'AEP' and 'DUR' are user defined batch file variables. The 'AEP' refers to an event magnitude (Annual Exceedance Probability) and 'DUR' refers to an event duration.

<li>Save the batch file and double click it in file explorer to run the simulation.
</ol> 

{{Tips Navigation
|uplink=[[Tutorial_Introduction| Back to Tutorial Introduction Main Page]]
}}

Tutorial M08

2026-01-05T02:46:45Z

Abrar.Alttahir: /* Introduction */

= Introduction =
In this module, scenario management is introduced. This is a powerful functionality running different simulations using a single set of control files rather than creating a new set of control files for every simulation. This makes the management of the model easier, ensures consistency between the simulations and better quality control. Without implementing scenario management, six TCFs and six TGC files are required to run this module, using scenario management, only one each is needed: 
 
[[File:M08 ScenarioManagement 02.png]] Scenarios are set up using an if scenario logic block - <tt>If Scenario</tt> command followed by <tt>Else If Scenario</tt> commands adding on different scenarios and terminated by <tt>End If</tt>.

Note: Scenarios can be added to most of TUFLOW’s control files. The only control files that do not support scenarios are the TUFLOW Rainfall Control File (.trcf), TUFLOW External Stress File (.tesf), Advection Control File (.adcf), or within a Define Control command inside a TUFLOW Operational Control (.toc) file.

The tutorial demonstrates two ways of using scenarios:
:*Part 1: Single Scenario - cell size.
:*Part 2: Multiple Scenarios - cell size and topography updates.

The GIS layers are:
:*TGC layers:
<ol><ol><li>2d_zsh: A layer used to modify Zpt elevations using points, lines and polygons.
<li>2d_mat: A layer used to define the land use (material) types within the developmental area.</ol></ol>

'''Module 8 builds from the model created in [[Tutorial_M02 | Module 2]]. The completed Module 2 model is provided in the Module_08\TUFLOW folder.'''

=Part 1 - Single Scenario=
Use scenario management to run the model with a range of different cell sizes, initial timesteps and SGS sample frequency within a single TCF. These steps are often used to conduct a cell size convergence sensitivity testing.

== Simulation Control Files ==
=== TUFLOW Geometry Control File (TGC) ===
<ol>
<li>Save a copy of '''M02_001.tgc''' as '''M08_001.tgc''' in the '''Module_08\TUFLOW\model''' folder. 
<li>Open the '''M08_001.tgc''' in a text editor.
<li>Comment out or delete the 'cell size' command and add the following if scenario logic block: 
<tt>! Cell Size == 5 ! 2D cell size in metres</tt> 
<tt>If Scenario </tt> <tt>== </tt> <tt>10m </tt> <tt> ! Beginning of if statement - 10m scenario</tt> 
<ol><tt>Cell Size </tt> <tt>== </tt> <tt>10 </tt> <tt> ! 2D cell size in metres - 10 metres</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>5m </tt> <tt> ! 5m scenario</tt> 
<ol><tt>Cell Size </tt> <tt>== </tt> <tt>5 </tt> <tt> ! 2D cell size in metres - 5 metres</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>2.5m </tt> <tt> ! 2.5m scenario</tt> 
<ol><tt>Cell Size </tt> <tt>== </tt> <tt>2.5 </tt> <tt> ! 2D cell size in metres - 2.5 metres</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

<li>Save the TGC.
</ol>
Note: The <tt>Pause </tt> command causes TUFLOW to stop every time it encounters it. In the example above, if a cell size was specified other than 10m, 5m, or 2.5m, 'Error: Scenario Name Not Valid' would be displayed. The simulation can then be continued or discontinued via a dialog window.

=== TUFLOW Control File (TCF) ===
<ol>
<li>Save a copy of '''M02_5m_001.tcf''' as '''M08_~s1~_001.tcf''' in the '''Module_08\TUFLOW\runs''' folder.
<li>Open the file '''M08_~s1~_001.tcf''' in a text editor.

<li>Update the TGC reference: 
<tt>Geometry Control File </tt> <tt>== </tt> <tt>..\model\M08_001.tgc </tt> <tt> ! Reference the TUFLOW Geometry Control File</tt> 
<li>The Timestep is dependent on the model cell size. A general guide is to use 1/2 to 1/5 of the model cell size in metres. Comment out or delete the 'Timestep' command and add the following if scenario logic block: 
<tt>! Timestep == 1 ! Specifies the first 2D computational timestep of 1 second</tt> 
<tt>If Scenario </tt> <tt>== </tt> <tt>10m </tt> <tt> ! Beginning of if statement - 10m scenario</tt> 
<ol><tt>Timestep </tt> <tt>== </tt> <tt>2 </tt> <tt> ! Specifies the first 2D computational timestep of 2 seconds</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>5m </tt> <tt> ! 5m scenario</tt> 
<ol><tt>Timestep </tt> <tt>== </tt> <tt>1 </tt> <tt> ! Specifies the first 2D computational timestep of 1 second</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>2.5m </tt> <tt> ! 2.5m scenario</tt> 
<ol><tt>Timestep </tt> <tt>== </tt> <tt>0.5 </tt> <tt> ! Specifies the first 2D computational timestep of 0.5 seconds</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

<li>Save the TCF.
</ol>

== Running the Simulation ==
<ol>
<li>Save a copy of '''_run_M02_HPC.bat''' as '''_run_M08_HPC.bat''' in the '''Module_08\TUFLOW\runs''' folder.
<li>Update the batch file to reference the '''M08_~s1~_001.tcf''' and include a scenario switch (-s1) defining cell size: 
<tt>'''set'''</tt> <tt>exe</tt><tt>=</tt><tt>"..\..\..\exe\2025.1.0\TUFLOW_iSP_w64.exe"</tt> 
<tt>'''set'''</tt> <tt>run</tt><tt>=</tt><tt>start "TUFLOW" /wait</tt> <tt> %exe%</tt> <tt> -b</tt> 
<tt>%run% </tt> <tt> -s1 10m M08_~s1~_001.tcf </tt> 
<tt>%run% </tt> <tt> -s1 5m M08_~s1~_001.tcf </tt> 
<tt>%run% </tt> <tt> -s1 2.5m M08_~s1~_001.tcf </tt> 
<li>Save the batch file and double click it in file explorer to run the simulation.
</ol>

== Troubleshooting ==
See tips on common mistakes and troubleshooting steps if the model doesn't run:
:*[[Tutorial_Troubleshooting_QGIS | QGIS]]

== Check Files ==
While the model is running, review the added features are specified correctly:
:*[[Tutorial_M08_001_Check_Files_QGIS | QGIS - SHP]]
:*[[Tutorial_M08_001_Check_Files_QGIS_GPKG | QGIS - GPKG]]
Compare the model cell count:
<ol>
<li>Navigate to the '''Module_08\TUFLOW\runs\log''' folder and open the following files in a text editor:
:*'''M08_10m_001.tlf'''
:*'''M08_5m_001.tlf'''
:*'''M08_2.5m_001.tlf'''
<li>Search for 'Number 2D Cells' and view the number of active cells for each model. This is the number of active cells as specified by the 2d_code layer. There are approximately 16 times more cells in the 2.5m model compared to the 10m model:
:*M08_10m_001: 5122
:*M08_5m_001: 20486
:*M08_2.5m_001: 81869
</ol>

== Results==
When the model is finished, review the results:
:*[[Tutorial_M08_001_Results_QGIS | QGIS]]
Compare the model run times:
<ol>
<li>Navigate to the '''Module_08\TUFLOW\runs''' folder and open the '''_TUFLOW Simulations.log''' in a text editor.
<li>The file shows the simulation times for all the simulations.
 
[[File:M08_TUFLOW_Simulations_f.png]] 
 
</ol>
 

=Part 2 - Multiple Scenarios=
Use scenario management to run the model with a range of different cell sizes and topography changes within a single TCF. These steps are often used to conduct an impact assessment.
 

== GIS Inputs ==
Create, import and view input data: 
:*[[Tutorial_M08_002_GIS_Inputs_QGIS | QGIS - SHP]] 
:*[[Tutorial_M08_002_GIS_Inputs_QGIS_GPKG | QGIS - GPKG]] 

== Simulation Control Files ==
=== TUFLOW Geometry Control File (TGC) ===
<ol>
<li>Save a copy of '''M08_001.tgc''' as '''M08_002.tgc''' in the '''Module_08\TUFLOW\model''' folder. 
<li>Open the '''M08_002.tgc''' in a text editor.
<li>Include the following if scenario logic block for existing and developed case after the '<tt>Read GIS Z Shape</tt>' command: 

'''QGIS - SHP''' 
<tt>If Scenario </tt> <tt>== </tt> <tt>EXG </tt> <tt> ! Beginning of if statement - existing scenario</tt> 
<ol><tt> ! Defines a placeholder</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>DEV </tt> <tt> ! Development scenario</tt> 
<ol><tt>Read GIS Z Shape </tt> <tt>== </tt> <tt>gis\2d_zsh_M02_landscape_002_R.shp | gis\2d_zsh_M02_landscape_002_L.shp | gis\2d_zsh_M02_landscape_002_P.shp </tt> <tt> ! Defines areas of complex landscaping</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

'''QGIS - GPKG''' 
<tt>If Scenario </tt> <tt>== </tt> <tt>EXG </tt> <tt> ! Beginning of if statement - existing scenario</tt> 
<ol><tt> ! Defines a placeholder</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>DEV </tt> <tt> ! Development scenario</tt> 
<ol><tt>Read GIS Z Shape </tt> <tt>== </tt> <tt>2d_zsh_M02_landscape_002_R | 2d_zsh_M02_landscape_002_L | 2d_zsh_M02_landscape_002_P </tt> <tt> ! Defines areas of complex landscaping</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

<li>Include the following if scenario logic block for existing and developed case after the '<tt>Read GIS Mat</tt>' command: 

'''QGIS - SHP''' 
<tt>If Scenario </tt> <tt>== </tt> <tt>EXG </tt> <tt> ! Beginning of if statement - existing scenario</tt> 
<ol><tt> ! Defines a placeholder</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>DEV </tt> <tt> ! Development scenario</tt> 
<ol><tt>Read GIS Mat </tt> <tt>== </tt> <tt>gis\2d_mat_M02_landscape_002_R.shp </tt> <tt> ! Sets material values according to attributes in the GIS layer</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

'''QGIS - GPKG''' 
<tt>If Scenario </tt> <tt>== </tt> <tt>EXG </tt> <tt> ! Beginning of if statement - existing scenario</tt> 
<ol><tt> ! Defines a placeholder</tt> </ol>
<tt>Else If Scenario </tt> <tt>== </tt> <tt>DEV </tt> <tt> ! Development scenario</tt> 
<ol><tt>Read GIS Mat </tt> <tt>== </tt> <tt>2d_mat_M02_landscape_002_R </tt> <tt> ! Sets material values according to attributes in the GIS layer</tt> </ol>
<tt>Else </tt> <tt> ! Else statement</tt> 
<ol><tt>Pause </tt> <tt>== </tt> <tt>Error: Scenario Name Not Valid </tt> <tt> ! Displays specified text if different scenario name is called </tt> </ol>
<tt>End If </tt> <tt> ! End of if statement</tt> 

<li>Save the TGC.
</ol>

=== TUFLOW Control File (TCF) ===
<ol>
<li>Save a copy of '''M08_~s1~_001.tcf''' as '''M08_~s1~_~s2~_002.tcf''' in the '''Module_08\TUFLOW\runs''' folder.
<li>Open the file '''M08_~s1~_~s2~_002.tcf''' in a text editor.
<li>Update the TGC reference: 
<tt>Geometry Control File </tt> <tt>== </tt> <tt>..\model\M08_002.tgc </tt> <tt> ! Reference the TUFLOW Geometry Control File</tt> 
<li>For GeoPackage users, update the Spatial Database reference: 
'''QGIS - GPKG''' 
<tt>Spatial Database </tt> <tt>== </tt> <tt>..\model\gis\M08_002.gpkg</tt> <tt> ! Specify the location of the GeoPackage Spatial Database</tt> 
<li>Save the TCF.
</ol>

== Running the Simulation ==
<ol>
<li>Open '''_run_M08_HPC.bat''' from the '''Module_08\TUFLOW\runs''' folder.
<li>Update the batch file to reference the '''M08_~s1~_~s2~_002.tcf''' and in addition to cell size scenario switch (-s1) include second scenario switch (-s2) defining topography: 
<tt>'''set'''</tt> <tt>exe</tt><tt>=</tt><tt>"..\..\..\exe\2025.1.0\TUFLOW_iSP_w64.exe"</tt> 
<tt>'''set'''</tt> <tt>run</tt><tt>=</tt><tt>start "TUFLOW" /wait</tt> <tt> %exe%</tt> <tt> -b</tt> 
<tt>%run% </tt> <tt> -s1 2.5m -s2 EXG M08_~s1~_~s2~_002.tcf </tt> 
<tt>%run% </tt> <tt> -s1 2.5m -s2 DEV M08_~s1~_~s2~_002.tcf </tt> 
<tt>%run% </tt> <tt> -s1 5m -s2 EXG M08_~s1~_~s2~_002.tcf </tt> 
<tt>%run% </tt> <tt> -s1 5m -s2 DEV M08_~s1~_~s2~_002.tcf </tt> 
<tt>%run% </tt> <tt> -s1 10m -s2 EXG M08_~s1~_~s2~_002.tcf </tt> 
<tt>%run% </tt> <tt> -s1 10m -s2 DEV M08_~s1~_~s2~_002.tcf </tt> 
<li>Save the batch file and double click it in file explorer to run the simulation. 
</ol>

== Troubleshooting ==
See tips on common mistakes and troubleshooting steps if the model doesn't run:
:*[[Tutorial_Troubleshooting_QGIS | QGIS]]

== Check Files ==
While the model is running, review the added features are specified correctly:
:*[[Tutorial_M08_002_Check_Files_QGIS | QGIS]]

== Results==
When the model is finished, review the results:
:*[[Tutorial_M08_002_Results_QGIS | QGIS]]
 

= Conclusion =
:*Single and multiple scenarios were set up and run with a range of cell sizes and designs.
:*Check files were assessed to view the changes based on each scenario.
:*Multiple scenario results were viewed using the TUFLOW Viewer.
:*For further training opportunities see [https://tuflow.com/training/training-course-catalogue/ TUFLOW Training Catalogue] and/or contact [mailto:training@tuflow.com training@tuflow.com]. 
:*Alternatively, see the [[TUFLOW_Example_Models#Example_Model_Catalogue | TUFLOW Example Models]] to explore the full list of TUFLOW features.
 

=Looping Batch File (Optional)=
Looping batch file is a powerful tool running large number of simulations with the least amount of batch file text:
<ol>
<li>Save a copy of '''_run_M08_HPC.bat''' as '''_run_M08_HPC_loop.bat''' in the '''Module_08\TUFLOW\runs''' folder.
<li>Add the following commands. Indentation is important in batch files, insert tabs to match the below spacing: 
<tt>echo </tt> <tt>off</tt> 
<tt>set </tt> <tt>exe</tt><tt>=</tt><tt>"..\..\..\exe\2025.1.0\TUFLOW_iSP_w64.exe"</tt> 
<tt>set </tt> <tt>run</tt><tt>=</tt><tt>start "TUFLOW" /wait </tt><tt>%exe%</tt><tt> -b</tt> 
 
<tt>set</tt> <tt> CSZ</tt><tt>=</tt><tt>10m 5m 2.5m</tt> 
<tt>set</tt> <tt> DES</tt><tt>=</tt><tt>EXG DEV</tt> 
 
<tt>for </tt><tt>%%a</tt><tt> in </tt><tt>(</tt><tt>%CSZ%</tt><tt>)</tt><tt> do </tt><tt>(</tt> 
<ol><tt> for </tt><tt>%%b</tt><tt> in </tt><tt>(</tt><tt>%DES%</tt><tt>)</tt><tt> do </tt><tt>( </tt> 
<ol><ol><tt> echo </tt><tt>Running </tt><tt>%%a %%b </tt> 
<tt> %run% </tt><tt>-s1 </tt><tt>%%a</tt><tt> -s2 </tt><tt>%%b </tt><tt>M08_~s1~_~s2~_002.tcf</tt> 
</ol></ol> ) 
</ol>) 
Note: The 'CSZ' and 'DES' are user defined batch file variables. The 'CSZ' refers to a cell size and 'DES' refers to a design scenario. 

<li>Save the batch file and double click it in file explorer to run the simulation.
</ol> 

{{Tips Navigation
|uplink=[[Tutorial_Introduction| Back to Tutorial Introduction Main Page]]
}}

TUFLOW 1D Channels and Hydraulic Structures

2025-11-13T02:16:17Z

Abrar.Alttahir: /* How can a flow splitter with a nib wall be modelled? */

=Introduction=
The objective of the following pages is to supplement the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW Manual] and provide additional modelling guidance on 1D hydraulic channels.
=1D Channel Types=
1d_nwk channels represent open channels, hydraulic structures, operational structures and other flow controls. A channel is either digitised as a line or point with the relevant hydraulic properties entered into the appropriate GIS attributes, details for this can be found within the links below or the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW Manual].
==Open Channels==
{| class="wikitable" width="50%"

! style="background-color:#005581; font-weight:bold; color:white;"| Channel Type
! style="background-color:#005581; font-weight:bold; color:white;" width=75%| Description of page contents
|-
| [[ 1D_Open_Channels | Open Channels]]|| This page contains information on basic commands, error checking and common check files used.
|}

==Structures==
The table below contains a complete list of 1D structures available within TUFLOW, including logic control structures. 
{| class="wikitable" width="50%"

! style="background-color:#005581; font-weight:bold; color:white;"| Channel Type
! style="background-color:#005581; font-weight:bold; color:white;" width=75%| Description of page contents
|-
| [[1D_Culverts | Culverts]]|| 1D-2D connections, flow regimes, operational control and common check files used.
|-
| [[1D_Bridges | Bridges]]|| Loss theory, irregular shaped bridges and common check files used.
|-
| [[1D_Weirs | Weirs]]|| Weir types
|-
| [[ 1D_Pumps | Pumps]]|| Pump attributes, 2D-2D & 1D-2D configurations, Estry setup, depth-discharge database and 1D results file.
|-
| [[1D_Pits | Pits]]|| Pit inlet types, depth-discharge data sources, modelling advice.
|-
| [[1D_Manholes | Manholes]]|| Losses and storage chambers.
|-
|[[1D Syphons|Syphons]]
|1D configuration, losses.
|-
|}

= Common Questions Answered (FAQ)=
== What is the difference between 1d_pit and 1d_nwk layer for pits? ==
The 1d_pit layer is a newer and simplified version of the 1d_nwk layer for pits. 
=== 1d_pit ===
* Assumes default values for any of missing attributes that are described for the 1d_nwk pits layers.
* Does not have invert attributes. The upstream invert is assumed to be the ZC elevation of the connected 2D SX cell, as such the "L" flag is not supported. The downstream invert needs to retrieve its value from the connected 1d_nwk channel.
* The nodal area assumes the default, the downstream pit channel node has its nodal area automatically assigned based on the connected 1d_nwk channel segments that have the UCS attribute set to “true”. The applied nodal area can be confirmed in the .eof file. 
* Virtual pipes can only be setup with the 1d_pit layer.
=== 1d_nwk ===
* Used for models that needs to deviate from the default approaches.

== How can I read approach-capture flow curves for on-grade pits into TUFLOW? ==
TUFLOW currently doesn't support the use of approach-capture flow curves directly. Manning's equation calculations can be used to convert on-grade approach flow information to a water depth at the pit. For example, [https://www.arr-software.org/arrdocs.html Australian Rainfall and Runoff 2019] (Engineers Australia) (Book 9, Chapter 5, Section 5.6.1) recommends using the modified Mannings equation, the Izzard's equation, for gutter and roadway flow.

The pit depth-flow curve relationship can be derived as follows:
*Select a few representative slope increments for the project area and conduct a Manning's equation calculation for each slope to calculate the gutter flow rates for incrementally increasing flow depths.
*Multiply the calculated flows by the % value in the on grade curve. This turns the % capture vs. flow curve into a depth vs. flow relationship.
*Repeat for different gutter shapes

== Should I model culverts in the 1D or 2D domain? ==
This largely depends on the size of the culvert in comparison with the cell size. Most commonly culverts are modelled within the 1D domain, however, with more computational power given by GPU devices, cell sizes are getting smaller and it might be beneficial to model bigger culverts in 2D. If the culvert is an important aspect of the study, it is always recommended to model it in number of ways by either using different methods and/or different software and the afflux is cross-checked against a desktop analysis and hand calculations. None will be 100% right, but the intent here is to establish a verification of the preferred approach. 
=== 2D only approach ===
* Benefits:
** Good stability and improved hydraulic behaviour at the culvert entrance/exit.
** The expansion loss (exit loss) can be explicitly handled in 2D provided the 2D cell resolution is sufficiently fine to model the expansion of flow downstream of the culvert.
* Challenges:
** The contraction loss (entry loss), which is related to the expansion of water after the vena-contracta and forms inside at the culvert inlet, won't be picked up well and some additional energy loss (form loss) might be needed to cover this.
** Side and soffit wall friction are not modelled unless Manning's n varies with depth, i.e. increase Manning's n with depth to account for the missing Manning's n on the side and top surfaces.
** The vertical walls not only create extra friction but also straightens the flow in the direction of the wall. Thin breaklines can be used to represent these walls in 2D, but it is likely to cause saw-tooth effect if sub-grid sampling (SGS) is not used, i.e. extra numerical head loss, if there are too few cells between the walls. SGS is recommended in this case.
** Flow overtopping can be represented to some extent by assuming 100% blockage of layer 2 in a 2d_lfcsh, but the flow upstream and downstream before the culvert is overtopped is hard to model.

=== 2D-1D-2D approach ===
* Benefits:
** A more appropriate approach where the 2D cell size is greater than around half the total culvert width.
** Contraction losses (entry losses) are handled better.
** Flow overtopping can be modelled in 2D.
* Challenges:
** Expansion losses (exit losses) are very dependent on the 2D cell resolution. A 2D cell size much larger than the culvert width will not reproduce the expansion losses very well (even with SGS) and the culvert's exit loss needs to cover this. A finer 2D cell size (several or more cells across the culvert) will reproduce the expansion losses much better and the culvert's exit loss need to be reduced to compensate it. This usually only happens for large 1D culverts with high velocities as there can be losses duplicated in the 2D on the exit side as the 2D flow expands (i.e. duplication of the exit/expansion loss). The latest release has a new feature to automatically adjust 1D culvert losses based on the 2D approach/departure velocities as what happens with a 1D-1D-1D arrangement. See this [https://downloads.tuflow.com/_archive/Publications/Modelling%20of%20Bends%20and%20Hydraulic%20Structures%20in%20a%202D%20Scheme,%20Syme,%202001.pdf paper] for more information.
** HX connections may cause instability, especially with a skewed culvert outlet, but can produce better velocity patterns downstream of the culvert to model the expansion losses, which will be occurring in the 2D domain.
** Given that the SX connection applies the flow going out of the 1D culvert as a source term without momentum, it is difficult to completely prevent the water from piling up. If required, wingwalls can be modelled as thin breaklines to help guide the water away. Using the SX boundary Z flag lowers other SX cells below the 1D culvert invert level and it can mitigate the water from piling up.

== What entry/exit loss and contraction coefficients should I use for 1D culverts? ==
We don’t provide hard recommendations on the exit and entry losses to use for culverts as we have found different organisations around the world, typically government, have their own guidelines for different types of inlets configurations and require these to be used, for example, the [https://www.ipwea-qnt.com/products-resources/qudm/ Queensland Urban Drainage Manual] (QUDM). However, it is very important to understand how losses are applied and that different 1D solvers may treat them differently. For cross-checking your results from any hydraulic modelling software, a simple calculation applying the entry and exit losses (allowing for any automatic adjustments as discussed below) to the computed head (V2/2g), plus allowing any surface roughness losses (Manning's equation) for longer culverts, is the best practice for culverts flowing in a sub-critical flow condition (i.e. downstream controlled flow). 

For the entrance loss values, the approach should be to use values as quoted in the literature or guidelines for the inlet shape and design unless there is evidence to use another value (e.g. comparison with reliable calibration data would indicate different energy losses).

For the exit loss a value of 1.0 is recommended in nearly all situations provided losses are being adjusted every timestep. A value of 1.0 with adjusted losses is derived from the fluid flow physics (momentum or energy conservation) for expanding flow and will be the most precise approach for modelling exit losses or to apply a bend loss to the approach/departure channel if these are modelled in 1D.

Occasionally there are situations where non-standard entrance and exit loss values are needed. A good example is if the approach or departure flow is skewed to the culvert direction. In these situations there may also be a significant bend (energy) loss occurring as the water changes direction entering or leaving the structure. To account for this the modeller may need to increase the entrance and/or exit loss values. 

By default, TUFLOW adjusts the entrance and exit losses of 1D structures flowing sub-critical every timestep based on the approach/departure velocities for 1D-1D-1D and with a new feature in the 2020-10-AA build for 2D-1D-2D. The entrance losses are adjusted based on an empirical relationship from flume testing whilst the exit loss equation is theoretically derived as mentioned above - refer to the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW Manual]. The modeller should be familiar with the approach taken by the software they are using as some software either don't adjust for approach/departure velocities at structures (will overestimate losses using standard values) and some may apply a limiting loss thereby not allowing the losses to sufficiently reduce.

TUFLOW, by default, allows the losses to reduce to effectively a zero loss coefficient (i.e. 0.0001). A zero loss occurs where the approach and departure velocity is the same as the structure velocity. For example, a clear-spanning bridge over a concrete lined channel with the water level below the bridge deck will experience no energy losses until the bridge deck is surcharged so if your software is applying unadjusted or limited energy loss coefficients there will be an unrealistic energy loss at the structure for flow below the bridge deck. For culverts, in most cases there will be some losses as it is rare that the channel is of identical shape and slope to the culvert with usually the culvert being more constrictive and therefore a higher velocity so the adjusted coefficients are nearly always non-zero. At the other extreme is flow from or into a near still body of water (e.g. a lake or the ocean). In this situation the loss coefficient(s) will not be reduced and the maximum energy loss possible should occur.

If the default adjust losses approach is used (<tt>Structure Losses</tt> <tt>==</tt><tt> ADJUST</tt>) the recommendations are to use industry guidelines for the entrance loss coefficient based on the shape/design of the inlet (these coefficients are typically based on a near zero approach velocity), and to use 1.0 for the exit loss coefficient. This applies to 1D culverts connected to 1D channels. The adjusted entrance and exit losses can be viewed over time in the _TSL layer, refer to the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW Manual]. 

Since the 2020-10-AA release, TUFLOW has a new (beta functionality) to have the losses automatically adjusted for linked 1D culverts and other structures connected to 2D domains through SX links (<tt>Structure Losses SX</tt> <tt>==</tt><tt> ADJUST</tt>), see Section 6.2 from the [https://downloads.tuflow.com/TUFLOW/Releases/2020-10/TUFLOW%20Release%20Notes.2020-10-AA.pdf 2020-10 Release Notes]. 

The other values to consider for modelling culverts are the inlet contraction coefficients used when the flow is upstream controlled flow should this occur. Typically the TUFLOW default values for these values should be used unless the inlet shape and design indicates otherwise. 

== Should I model pits as R or Q type? ==
Q pits are recommended for modelling kerb inlets, catchpits, drains, etc., provided that appropriate y-Q curves are available. TUFLOW automatically extends the y-Q curve for higher depths based on the orifice equation. Under downstream controlled flow regime it reverts to a drowned condition. 
When using the R pit, whilst the pit is free-falling into the pipe system below (most of the time until the system surcharges), entrance/exit losses don't apply, because the flow is inlet controlled. The R pit would be using the weir equation for unsubmerged flow or orifice flow for submerged flow and could be a poor representation of the pit flow behaviour, especially if the pit also has a grate and other non-rectangular characteristics.

== How to model pipe network with some pipe data missing? ==
In situations where the complete pipe network is not modelled there are numerous common approaches adopted by modellers:
* Use direct rainfall boundary conditions, model all inlets, only model the larger pipes in the pipe network. Where a small pipe is being omitted, connect the flows from the inlet to the main pipe network using virtual pipes functionality. Only the location of the inlet pits are needed, they can be approximated and sometimes lumped representation of the pits. This approach is especially good if the pipe network does not surcharge back to the overland flow anywhere or only in a minor way.
* Use lumped hydrology inflows. Only model the main pipe network and connected inlets (excluding the smaller features). Apply the flows directly to the pits either with:
** 1d_bc QT polygon boundaries with a P flag. This automatically distributes the total flow to all the pits within the polygon.
** 2d_sa boundaries with a "Read GIS SA PITS" command. The flow is applied to the ground surface rather than directly into the manhole. Some of the flow will surcharge out of the pits if the downstream pipe capacity is exceeded.

== How can I get more inflow into the pipe system? ==
A shallow depth can occur in the pit inlet cell and underestimate the depth at the entrance to the pit, restricting the amount of flow entering the pit. This can happen specifically for larger cell sizes. Different options to capture more water from 2D cells into the pits are available based on the TUFLOW engine. Both approaches aim to account for changes in ground elevation that occur at a finer scale than the 2D cell resolution:
*TUFLOW HPC - use Sub-Grid Sampling (SGS) - this approach samples underlying DEM with finer resolution and represents the true topography more accurately guiding water into the pits.
*TUFLOW Classic - use "Pit Default Road Crossfall" command to increase the depth at Q pits based on the crossfall slope of the road cross section. TUFLOW calculates the water depth that is used by the Pit Inlet Database using an adjacent side triangle depth approach, instead of the actual cell flow depth.

== How is flow through the culvert calculated? ==
Computationally, information is passed between the 1D and 2D on the half timestep:
* Flow through the culvert is calculated based on the water levels at the inlet/outlet and the assigned flow regime. The volume is extracted/added to the 2D boundary cells on the half timestep.
* On the next full timestep the 1D and 2D shallow water equations are applied to move water through their respective domains.

== What should I do if 1D pump doesn't convey as much flow as expected and/or seems to be unstable? ==
A couple of things can be checked:
* The inlet of the pipe has to be fully submerged, otherwise the pump will shut down and there will be no flow through the pump.
* Add extra storage to the node upstream of the pump with 1d_na table or 1d_nwk type NODE through ANA attribute.
* Try reasonably smaller 1D timestep.
* If using non-operational pump (P), check if the depth discharge relationship is appropriate. If outlet of the pipe is lower than inlet, the pump will always try to pump at full capacity.
* Consider using operational pump (PO), where the pump would switch off if the water level upstream gets below the pump soffit.

== How BB bridges automatically switch between pressure flow and being drowned out? ==
TUFLOW considers the top of the XZ or HW table as the soffit level. If the downstream water level exceeds the soffit level, TUFLOW does the pressure flow calculation using default K = 1.56. It compares the velocity calculated by the pressure flow equation and by the form loss calculation based on LC table and uses the smaller one.
The _TSF and _TSL layers can be used to find out the regime/form loss values used for BB bridges:
*"P": pressure flow
*"D": ds water level > the soffit level, but it applies normal flow because the normal flow equation predicts smaller velocity
*" ": ds water level < the soffit level, normal flow

== Should I model bridges in the 1D or 2D Domain? ==
The recommended approach typically depends on the study objectives and if the channel upstream and downstream of the bridge is modelled in 1D or 2D. To preserve the momentum as accurately as possible the bridge should be modelled in the same dimension as the channel, e.g. 1d_nwk bridge if the channels is in 1D and 2d_bg or 2d_lfcsh if the channel is modelled in 2D. 
In 2D, the expansion/contraction losses are modelled based on the topography and don't need to be estimated as attributes as for 1D modelling. Also, for higher flows where the bridge is overtopped, 2D is preferable approach.

== What is the difference between downstream and upstream controlled flow? ==
Downstream control means a change in downstream water level will cause a change in upstream water level. Upstream control means the upstream water level is insensitive to the downstream water level and usually indicates the occurrence of supercritical flow.

== How do negative "M", "N" and "R" values deactivate 1D cross-sections? ==
When negative “M” values are used to deactivate 1D cross-sections, it changes the flow area, wetted perimeter, hydraulic radius, and the conveyance, while keeping the storage width unchanged. It is like inserting a thin plate at the middle of the channel, as shown in the figure below. This approach is preferred to retain the storage in the system but reduce the conveyance (e.g., off-stream storage not contributing to conveyance or sections either side of a constriction to generate better approach/departure velocities if using the Total Area approach). 
'''Note:''' The negative "M" value must be a negative of a valid material ID. 
 
<ol>
[[File:Section deactivation M.png | 200px]] 
</ol>
When using negative (-1) “N” (Manning’s n) or “R” (relative resistance factor) to deactivate the 1D cross-sections, it reduces both the flow area and the storage width, as shown in the figure below. 
 
<ol>
[[File:Section deactivation N R.png | 200px]]
</ol>

==Calculating a 1d_bc head-discharge curve==

As of the 2023-03-AD release, it is possible for TUFLOW to automatically create a Head-Discharge curve for 1d_bc HQ boundaries. For more information see Section 3.5 of the [https://docs.tuflow.com/classic-hpc/release/2023-03-AE/OneDSolv-1.html#Auto1DHQ-2 2023-03 Release Notes].

If using older releases, this can be calculated manually as the condition to be satisfied for a normal depth is the Manning’s equation. The HQ depth should be set up with elevation and the discharge calculated from Manning’s equation (including Area and Hydraulic Radius for each elevation point) and can be calculated in a few steps: 

<ol>
<li>Set the model up your ESTRY model using a dummy downstream level boundary (e.g. a HQ boundary with no name, an error will be reported however the required check file will have been produced, otherwise any boundary will work) and run the model in test model (-t) so that check files are produced.
<li>From the check folder, open the '''*_1d_ta_tables.csv''' file.
<li>Find the entry for the most downstream cross-section (directly upstream of the 1d_bc HQ point). The '''*_1d_ta_tables.csv''' will contain a table of hydraulic parameters. The K entry highlighted is the cross-section conveyance in m3/s and is calculated as: 

K = 1/n * A * R2/3 

Where
* A is the Eff Area
* R is the Radius
 
[[File:Ta tables example.png | 800px ]] 

<li>Determine a representative slope (S), this could be taken from the last two cross-sections.
<li>Calculate the downstream Discharge (this is the same as calculated discharge from Mannings equation): 

Q = K * SQRT(S) 

Where:
* S is a representative slope value for the downstream reach.
* K is obtained from the '''1d_ta_tables.csv'''

<li>Use the Elevation (Head) vs Discharge pairs to populate the HQ table.
</ol>

==Why is the maximum 1D water level higher than 2D water level?==
TUFLOW calculates storage at 1D nodes (including manholes) and calculates flux at pipe mid-sections. The storages at the manholes are saved in the format of ‘elevation vs nodal area’ tables, which can be reviewed in the .eof file: 
<ol>
[[File:1DpitHmax 1.png]] 
</ol>

The change in water volume in a manhole is calculated based on the inflow from the pit above (if there is one) and the inflow/outflow from the connected pipes. The change in water level is then calculated based on the change in volume and the nodal area at that elevation. 
<ol>
[[File:1DpitHmax 2.png | 300px ]] 
</ol>

TUFLOW extends the ‘elevation vs nodal area’ table by 5 m (the last 2 numbers in the table above), which allows for the calculation of pressurised pipe flow when a manhole is drowned. If the maximum 1D water level is higher than the DEM, that means the manhole was drowned and pressurised during the peak of the flood. 
<ol>
[[File:1DpitHmax 3.png | 300px ]] 
</ol>

==How to represent the hydraulic behavior at the outlet of a pipe, including headwalls and transitions to open channels? ==
In general, pipe outlet headwalls affect the exit loss at the pipe outlet, and this can be modeled by changing the 1d_nwk “ExitC_or_WSb” attribute - exit loss coefficient for outlet controlled flow. The default exit loss coefficient is 1.0, assuming there is no headwall. For more details on these factors, refer to the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW Manual].
More guidance on applying entry/loss and contraction coefficients is available [[TUFLOW_1D_Channels_and_Hydraulic_Structures#What_entry.2Fexit_loss_and_contraction_coefficients_should_I_use_for_1D_culverts.3F | here]].

Once the water is in the 2D domain, headwalls can also affect the local flow field. The modelled hydraulic behavior depends on the model cell size. If the model has grid size smaller than the length of the headwall, thin breaklines or 2d_zsh tin can be added to represent the geometry in the 2D model. If the grid size is larger than the headwall, it only calculates ‘cell-averaged’ velocity including the impact of the headwall.

==Calculating velocity and bed shear stress at a pipe outlet==
The velocity and bed shear stress reported in the 2D domain at a pipe outlet depends on the model cell size. If the cell size is small enough and any outlets structures (i.e. wingwalls) are properly represented by the 2d cell/face elevations, TUFLOW can reasonably represent the flow field at the pipe outlet and estimate the bed shear stress base on the manning’s equation. However, be aware that TUFLOW solves 2D depth-averaged shallow water equation, so there is limitation of using it to estimate bed shear stress around complex structures or geometry that could experience vertical acceleration or re-circulation.

== How do I model blockage for irregular shaped culverts or channels? ==
For irregular shaped culverts and channels (1d_nwk "I" type), it is possible to model blockage by using the Skew (in degrees) attribute in the 1d_xs layer. This reduces the effective flow width of the cross section by rotating it relative to the direction of flow and can be used to approximate reduced capacity.

A skew angle of 0 means the cross section is aligned with the flow. As the angle increases, the effective width is reduced. For example, a skew of 60 degrees results results in a 50% reduction (as cos(60) = 0.5). While this does reduce the effective width of the structure, it isn’t expected to see the same reduction to conveyance as conveyance depends on the Manning’s n value, flow area and hydraulic radius.

== Do calculations differ if I specify multiple culverts using the Number_of attribute, even if the total dimensions are the same as a single culvert? ==
Yes. This can slightly affect the modelling results.

The key difference between a single wide culvert and multiple culverts with the same total width is the wetted perimeter. Multiple culverts have a slightly larger wetted perimeter due to the internal walls, which increases friction in downstream-controlled regimes. While friction losses are generally small compared to entry and exit losses, the impact on hydraulic behaviour is usually minor.

However, it’s important to model the configuration that best represents the actual structure when assessing culvert performance.

== How do I model a scruffy dome? ==
A scruffy dome is a type of dome inlet screen installed over a drop inlet, commonly used in stormwater systems. These inlets can be modelled in TUFLOW using a 1D Q type pit with a depth–discharge curve that reflects the hydraulic performance of the dome.

[https://www.ipwea-qnt.com/products-resources/qudm/ QUDM] recommends using the lesser of the weir flow or orifice flow equations to define inlet capacity (see Section 7.5.4). A blockage factor is also recommended to account for potential debris build-up during flood events (see Table 7.5.1 Provision for blockage at kerb inlets). This factor may vary depending on local guidelines, so regional standards should be consulted.

Once the rating curve is calculated, it can be added to the pit inlet database and linked to the Q type pit.
 
<ol>
[[File:How_do_I_model_a_scruffy_dome_in_TUFLOW.png|300x300px]] 
</ol>

== How can trash screens be represented? ==
A trash screen is a physical barrier, typically made from bars or mesh, placed at the inlet or outlet of a culvert, pipe, or channel to prevent debris from entering or exiting the structure.

Trash screens can be represented by applying additional head losses and blockage effects in accordance with [https://www.ipwea-qnt.com/products-resources/qudm/ QUDM] guidance. The approach depends on the location of the screen and the degree of blockage.

Possible use cases include:

* Where the screen is attached directly to the culvert inlet or outlet, the losses are applied to the culvert itself.
* Where the screen is located upstream of a culvert inlet or downstream of a culvert outlet, it can be modelled as a short zero length CF, IF, or RF channel before (upstream) or after (downstream) the culvert.
* In all cases, the net flow area should be reduced to account for the bars and any debris blockage, either by adjusting the flow area directly or by using the pBlockage attribute.

For example, the image below could be represented as a rectangular shaped screen located upstream or downstream of a culvert, modelled as a short zero length RF (rectangular culvert with fixed losses) channel using a 1d_nwk GIS layer.
<ol>
[[File:Trash_screen_schematic.png|alt=|300x300px]]
</ol>

== How can fishway baffles in a culvert be modelled? ==
UNDER CONSTRUCTION

Fishway baffles are barriers inside a culvert that slow the water and help fish swim through.

These approaches could be used to represent them inside a model:

* Short culvert with smaller opening.
** A short culvert section is added with its bottom and top levels set to match the fish baffle. The width is set to the total width of the fish passage gaps. This represents the reduced space for water to pass through.

* Two culverts in a row.
** Two culverts are connected in series. The first is the normal culvert, and the second is a very short culvert with a smaller opening sized to the fish baffle gaps.

For both options, sensitivity testing is recommended because at low flows the way the culvert is defined can change the results. These methods do not include turbulence or detailed 3D water movement around the baffles. If accurate fish passage flows are required, a CFD 3D model can be used to determine the flows and those flows can then be applied using a 1D Q type channel.
<ol>
[[File:Rectangular culvert baffled.png|alt=|300x300px]]
</ol>

== How can a flow splitter with a nib wall be modelled? ==
UNDER CONSTRUCTION

A flow splitter with a nib wall can be modelled by introducing a 1D weir between the manhole and the relevant outlet pipe. The weir crest elevation should be set equal to the height of the nib wall within the chamber. This setup allows low flows to discharge through one pipe, while higher flows overtop the weir into a secondary outlet, replicating the behaviour of a physical flow splitter.

The main outlet pipes are connected through a manhole, which automatically applies appropriate entry and exit losses. For outlets that do not connect further downstream, entry and exit losses should be manually defined in the attributes.

Refer to the schematic example below for this configuration.
<ol>
[[File:Manhole flow splitter topview.png|alt=|300x300px]]
</ol>
 

{{Tips Navigation
|uplink=[[ TUFLOW_Modelling_Guidance | Back to TUFLOW Modelling Guidance]]
}}

File:Manhole flow splitter topview.png

2025-11-13T02:14:27Z

Abrar.Alttahir:

Top down schematic of a manhole flow splitter showing inlet, outlets, and internal nib wall.

TUFLOW 1D Channels and Hydraulic Structures

2025-11-13T01:14:25Z

Abrar.Alttahir: /* How can a flow splitter with a nib wall be modelled? */

=Introduction=
The objective of the following pages is to supplement the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW Manual] and provide additional modelling guidance on 1D hydraulic channels.
=1D Channel Types=
1d_nwk channels represent open channels, hydraulic structures, operational structures and other flow controls. A channel is either digitised as a line or point with the relevant hydraulic properties entered into the appropriate GIS attributes, details for this can be found within the links below or the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW Manual].
==Open Channels==
{| class="wikitable" width="50%"

! style="background-color:#005581; font-weight:bold; color:white;"| Channel Type
! style="background-color:#005581; font-weight:bold; color:white;" width=75%| Description of page contents
|-
| [[ 1D_Open_Channels | Open Channels]]|| This page contains information on basic commands, error checking and common check files used.
|}

==Structures==
The table below contains a complete list of 1D structures available within TUFLOW, including logic control structures. 
{| class="wikitable" width="50%"

! style="background-color:#005581; font-weight:bold; color:white;"| Channel Type
! style="background-color:#005581; font-weight:bold; color:white;" width=75%| Description of page contents
|-
| [[1D_Culverts | Culverts]]|| 1D-2D connections, flow regimes, operational control and common check files used.
|-
| [[1D_Bridges | Bridges]]|| Loss theory, irregular shaped bridges and common check files used.
|-
| [[1D_Weirs | Weirs]]|| Weir types
|-
| [[ 1D_Pumps | Pumps]]|| Pump attributes, 2D-2D & 1D-2D configurations, Estry setup, depth-discharge database and 1D results file.
|-
| [[1D_Pits | Pits]]|| Pit inlet types, depth-discharge data sources, modelling advice.
|-
| [[1D_Manholes | Manholes]]|| Losses and storage chambers.
|-
|[[1D Syphons|Syphons]]
|1D configuration, losses.
|-
|}

= Common Questions Answered (FAQ)=
== What is the difference between 1d_pit and 1d_nwk layer for pits? ==
The 1d_pit layer is a newer and simplified version of the 1d_nwk layer for pits. 
=== 1d_pit ===
* Assumes default values for any of missing attributes that are described for the 1d_nwk pits layers.
* Does not have invert attributes. The upstream invert is assumed to be the ZC elevation of the connected 2D SX cell, as such the "L" flag is not supported. The downstream invert needs to retrieve its value from the connected 1d_nwk channel.
* The nodal area assumes the default, the downstream pit channel node has its nodal area automatically assigned based on the connected 1d_nwk channel segments that have the UCS attribute set to “true”. The applied nodal area can be confirmed in the .eof file. 
* Virtual pipes can only be setup with the 1d_pit layer.
=== 1d_nwk ===
* Used for models that needs to deviate from the default approaches.

== How can I read approach-capture flow curves for on-grade pits into TUFLOW? ==
TUFLOW currently doesn't support the use of approach-capture flow curves directly. Manning's equation calculations can be used to convert on-grade approach flow information to a water depth at the pit. For example, [https://www.arr-software.org/arrdocs.html Australian Rainfall and Runoff 2019] (Engineers Australia) (Book 9, Chapter 5, Section 5.6.1) recommends using the modified Mannings equation, the Izzard's equation, for gutter and roadway flow.

The pit depth-flow curve relationship can be derived as follows:
*Select a few representative slope increments for the project area and conduct a Manning's equation calculation for each slope to calculate the gutter flow rates for incrementally increasing flow depths.
*Multiply the calculated flows by the % value in the on grade curve. This turns the % capture vs. flow curve into a depth vs. flow relationship.
*Repeat for different gutter shapes

== Should I model culverts in the 1D or 2D domain? ==
This largely depends on the size of the culvert in comparison with the cell size. Most commonly culverts are modelled within the 1D domain, however, with more computational power given by GPU devices, cell sizes are getting smaller and it might be beneficial to model bigger culverts in 2D. If the culvert is an important aspect of the study, it is always recommended to model it in number of ways by either using different methods and/or different software and the afflux is cross-checked against a desktop analysis and hand calculations. None will be 100% right, but the intent here is to establish a verification of the preferred approach. 
=== 2D only approach ===
* Benefits:
** Good stability and improved hydraulic behaviour at the culvert entrance/exit.
** The expansion loss (exit loss) can be explicitly handled in 2D provided the 2D cell resolution is sufficiently fine to model the expansion of flow downstream of the culvert.
* Challenges:
** The contraction loss (entry loss), which is related to the expansion of water after the vena-contracta and forms inside at the culvert inlet, won't be picked up well and some additional energy loss (form loss) might be needed to cover this.
** Side and soffit wall friction are not modelled unless Manning's n varies with depth, i.e. increase Manning's n with depth to account for the missing Manning's n on the side and top surfaces.
** The vertical walls not only create extra friction but also straightens the flow in the direction of the wall. Thin breaklines can be used to represent these walls in 2D, but it is likely to cause saw-tooth effect if sub-grid sampling (SGS) is not used, i.e. extra numerical head loss, if there are too few cells between the walls. SGS is recommended in this case.
** Flow overtopping can be represented to some extent by assuming 100% blockage of layer 2 in a 2d_lfcsh, but the flow upstream and downstream before the culvert is overtopped is hard to model.

=== 2D-1D-2D approach ===
* Benefits:
** A more appropriate approach where the 2D cell size is greater than around half the total culvert width.
** Contraction losses (entry losses) are handled better.
** Flow overtopping can be modelled in 2D.
* Challenges:
** Expansion losses (exit losses) are very dependent on the 2D cell resolution. A 2D cell size much larger than the culvert width will not reproduce the expansion losses very well (even with SGS) and the culvert's exit loss needs to cover this. A finer 2D cell size (several or more cells across the culvert) will reproduce the expansion losses much better and the culvert's exit loss need to be reduced to compensate it. This usually only happens for large 1D culverts with high velocities as there can be losses duplicated in the 2D on the exit side as the 2D flow expands (i.e. duplication of the exit/expansion loss). The latest release has a new feature to automatically adjust 1D culvert losses based on the 2D approach/departure velocities as what happens with a 1D-1D-1D arrangement. See this [https://downloads.tuflow.com/_archive/Publications/Modelling%20of%20Bends%20and%20Hydraulic%20Structures%20in%20a%202D%20Scheme,%20Syme,%202001.pdf paper] for more information.
** HX connections may cause instability, especially with a skewed culvert outlet, but can produce better velocity patterns downstream of the culvert to model the expansion losses, which will be occurring in the 2D domain.
** Given that the SX connection applies the flow going out of the 1D culvert as a source term without momentum, it is difficult to completely prevent the water from piling up. If required, wingwalls can be modelled as thin breaklines to help guide the water away. Using the SX boundary Z flag lowers other SX cells below the 1D culvert invert level and it can mitigate the water from piling up.

== What entry/exit loss and contraction coefficients should I use for 1D culverts? ==
We don’t provide hard recommendations on the exit and entry losses to use for culverts as we have found different organisations around the world, typically government, have their own guidelines for different types of inlets configurations and require these to be used, for example, the [https://www.ipwea-qnt.com/products-resources/qudm/ Queensland Urban Drainage Manual] (QUDM). However, it is very important to understand how losses are applied and that different 1D solvers may treat them differently. For cross-checking your results from any hydraulic modelling software, a simple calculation applying the entry and exit losses (allowing for any automatic adjustments as discussed below) to the computed head (V2/2g), plus allowing any surface roughness losses (Manning's equation) for longer culverts, is the best practice for culverts flowing in a sub-critical flow condition (i.e. downstream controlled flow). 

For the entrance loss values, the approach should be to use values as quoted in the literature or guidelines for the inlet shape and design unless there is evidence to use another value (e.g. comparison with reliable calibration data would indicate different energy losses).

For the exit loss a value of 1.0 is recommended in nearly all situations provided losses are being adjusted every timestep. A value of 1.0 with adjusted losses is derived from the fluid flow physics (momentum or energy conservation) for expanding flow and will be the most precise approach for modelling exit losses or to apply a bend loss to the approach/departure channel if these are modelled in 1D.

Occasionally there are situations where non-standard entrance and exit loss values are needed. A good example is if the approach or departure flow is skewed to the culvert direction. In these situations there may also be a significant bend (energy) loss occurring as the water changes direction entering or leaving the structure. To account for this the modeller may need to increase the entrance and/or exit loss values. 

By default, TUFLOW adjusts the entrance and exit losses of 1D structures flowing sub-critical every timestep based on the approach/departure velocities for 1D-1D-1D and with a new feature in the 2020-10-AA build for 2D-1D-2D. The entrance losses are adjusted based on an empirical relationship from flume testing whilst the exit loss equation is theoretically derived as mentioned above - refer to the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW Manual]. The modeller should be familiar with the approach taken by the software they are using as some software either don't adjust for approach/departure velocities at structures (will overestimate losses using standard values) and some may apply a limiting loss thereby not allowing the losses to sufficiently reduce.

TUFLOW, by default, allows the losses to reduce to effectively a zero loss coefficient (i.e. 0.0001). A zero loss occurs where the approach and departure velocity is the same as the structure velocity. For example, a clear-spanning bridge over a concrete lined channel with the water level below the bridge deck will experience no energy losses until the bridge deck is surcharged so if your software is applying unadjusted or limited energy loss coefficients there will be an unrealistic energy loss at the structure for flow below the bridge deck. For culverts, in most cases there will be some losses as it is rare that the channel is of identical shape and slope to the culvert with usually the culvert being more constrictive and therefore a higher velocity so the adjusted coefficients are nearly always non-zero. At the other extreme is flow from or into a near still body of water (e.g. a lake or the ocean). In this situation the loss coefficient(s) will not be reduced and the maximum energy loss possible should occur.

If the default adjust losses approach is used (<tt>Structure Losses</tt> <tt>==</tt><tt> ADJUST</tt>) the recommendations are to use industry guidelines for the entrance loss coefficient based on the shape/design of the inlet (these coefficients are typically based on a near zero approach velocity), and to use 1.0 for the exit loss coefficient. This applies to 1D culverts connected to 1D channels. The adjusted entrance and exit losses can be viewed over time in the _TSL layer, refer to the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW Manual]. 

Since the 2020-10-AA release, TUFLOW has a new (beta functionality) to have the losses automatically adjusted for linked 1D culverts and other structures connected to 2D domains through SX links (<tt>Structure Losses SX</tt> <tt>==</tt><tt> ADJUST</tt>), see Section 6.2 from the [https://downloads.tuflow.com/TUFLOW/Releases/2020-10/TUFLOW%20Release%20Notes.2020-10-AA.pdf 2020-10 Release Notes]. 

The other values to consider for modelling culverts are the inlet contraction coefficients used when the flow is upstream controlled flow should this occur. Typically the TUFLOW default values for these values should be used unless the inlet shape and design indicates otherwise. 

== Should I model pits as R or Q type? ==
Q pits are recommended for modelling kerb inlets, catchpits, drains, etc., provided that appropriate y-Q curves are available. TUFLOW automatically extends the y-Q curve for higher depths based on the orifice equation. Under downstream controlled flow regime it reverts to a drowned condition. 
When using the R pit, whilst the pit is free-falling into the pipe system below (most of the time until the system surcharges), entrance/exit losses don't apply, because the flow is inlet controlled. The R pit would be using the weir equation for unsubmerged flow or orifice flow for submerged flow and could be a poor representation of the pit flow behaviour, especially if the pit also has a grate and other non-rectangular characteristics.

== How to model pipe network with some pipe data missing? ==
In situations where the complete pipe network is not modelled there are numerous common approaches adopted by modellers:
* Use direct rainfall boundary conditions, model all inlets, only model the larger pipes in the pipe network. Where a small pipe is being omitted, connect the flows from the inlet to the main pipe network using virtual pipes functionality. Only the location of the inlet pits are needed, they can be approximated and sometimes lumped representation of the pits. This approach is especially good if the pipe network does not surcharge back to the overland flow anywhere or only in a minor way.
* Use lumped hydrology inflows. Only model the main pipe network and connected inlets (excluding the smaller features). Apply the flows directly to the pits either with:
** 1d_bc QT polygon boundaries with a P flag. This automatically distributes the total flow to all the pits within the polygon.
** 2d_sa boundaries with a "Read GIS SA PITS" command. The flow is applied to the ground surface rather than directly into the manhole. Some of the flow will surcharge out of the pits if the downstream pipe capacity is exceeded.

== How can I get more inflow into the pipe system? ==
A shallow depth can occur in the pit inlet cell and underestimate the depth at the entrance to the pit, restricting the amount of flow entering the pit. This can happen specifically for larger cell sizes. Different options to capture more water from 2D cells into the pits are available based on the TUFLOW engine. Both approaches aim to account for changes in ground elevation that occur at a finer scale than the 2D cell resolution:
*TUFLOW HPC - use Sub-Grid Sampling (SGS) - this approach samples underlying DEM with finer resolution and represents the true topography more accurately guiding water into the pits.
*TUFLOW Classic - use "Pit Default Road Crossfall" command to increase the depth at Q pits based on the crossfall slope of the road cross section. TUFLOW calculates the water depth that is used by the Pit Inlet Database using an adjacent side triangle depth approach, instead of the actual cell flow depth.

== How is flow through the culvert calculated? ==
Computationally, information is passed between the 1D and 2D on the half timestep:
* Flow through the culvert is calculated based on the water levels at the inlet/outlet and the assigned flow regime. The volume is extracted/added to the 2D boundary cells on the half timestep.
* On the next full timestep the 1D and 2D shallow water equations are applied to move water through their respective domains.

== What should I do if 1D pump doesn't convey as much flow as expected and/or seems to be unstable? ==
A couple of things can be checked:
* The inlet of the pipe has to be fully submerged, otherwise the pump will shut down and there will be no flow through the pump.
* Add extra storage to the node upstream of the pump with 1d_na table or 1d_nwk type NODE through ANA attribute.
* Try reasonably smaller 1D timestep.
* If using non-operational pump (P), check if the depth discharge relationship is appropriate. If outlet of the pipe is lower than inlet, the pump will always try to pump at full capacity.
* Consider using operational pump (PO), where the pump would switch off if the water level upstream gets below the pump soffit.

== How BB bridges automatically switch between pressure flow and being drowned out? ==
TUFLOW considers the top of the XZ or HW table as the soffit level. If the downstream water level exceeds the soffit level, TUFLOW does the pressure flow calculation using default K = 1.56. It compares the velocity calculated by the pressure flow equation and by the form loss calculation based on LC table and uses the smaller one.
The _TSF and _TSL layers can be used to find out the regime/form loss values used for BB bridges:
*"P": pressure flow
*"D": ds water level > the soffit level, but it applies normal flow because the normal flow equation predicts smaller velocity
*" ": ds water level < the soffit level, normal flow

== Should I model bridges in the 1D or 2D Domain? ==
The recommended approach typically depends on the study objectives and if the channel upstream and downstream of the bridge is modelled in 1D or 2D. To preserve the momentum as accurately as possible the bridge should be modelled in the same dimension as the channel, e.g. 1d_nwk bridge if the channels is in 1D and 2d_bg or 2d_lfcsh if the channel is modelled in 2D. 
In 2D, the expansion/contraction losses are modelled based on the topography and don't need to be estimated as attributes as for 1D modelling. Also, for higher flows where the bridge is overtopped, 2D is preferable approach.

== What is the difference between downstream and upstream controlled flow? ==
Downstream control means a change in downstream water level will cause a change in upstream water level. Upstream control means the upstream water level is insensitive to the downstream water level and usually indicates the occurrence of supercritical flow.

== How do negative "M", "N" and "R" values deactivate 1D cross-sections? ==
When negative “M” values are used to deactivate 1D cross-sections, it changes the flow area, wetted perimeter, hydraulic radius, and the conveyance, while keeping the storage width unchanged. It is like inserting a thin plate at the middle of the channel, as shown in the figure below. This approach is preferred to retain the storage in the system but reduce the conveyance (e.g., off-stream storage not contributing to conveyance or sections either side of a constriction to generate better approach/departure velocities if using the Total Area approach). 
'''Note:''' The negative "M" value must be a negative of a valid material ID. 
 
<ol>
[[File:Section deactivation M.png | 200px]] 
</ol>
When using negative (-1) “N” (Manning’s n) or “R” (relative resistance factor) to deactivate the 1D cross-sections, it reduces both the flow area and the storage width, as shown in the figure below. 
 
<ol>
[[File:Section deactivation N R.png | 200px]]
</ol>

==Calculating a 1d_bc head-discharge curve==

As of the 2023-03-AD release, it is possible for TUFLOW to automatically create a Head-Discharge curve for 1d_bc HQ boundaries. For more information see Section 3.5 of the [https://docs.tuflow.com/classic-hpc/release/2023-03-AE/OneDSolv-1.html#Auto1DHQ-2 2023-03 Release Notes].

If using older releases, this can be calculated manually as the condition to be satisfied for a normal depth is the Manning’s equation. The HQ depth should be set up with elevation and the discharge calculated from Manning’s equation (including Area and Hydraulic Radius for each elevation point) and can be calculated in a few steps: 

<ol>
<li>Set the model up your ESTRY model using a dummy downstream level boundary (e.g. a HQ boundary with no name, an error will be reported however the required check file will have been produced, otherwise any boundary will work) and run the model in test model (-t) so that check files are produced.
<li>From the check folder, open the '''*_1d_ta_tables.csv''' file.
<li>Find the entry for the most downstream cross-section (directly upstream of the 1d_bc HQ point). The '''*_1d_ta_tables.csv''' will contain a table of hydraulic parameters. The K entry highlighted is the cross-section conveyance in m3/s and is calculated as: 

K = 1/n * A * R2/3 

Where
* A is the Eff Area
* R is the Radius
 
[[File:Ta tables example.png | 800px ]] 

<li>Determine a representative slope (S), this could be taken from the last two cross-sections.
<li>Calculate the downstream Discharge (this is the same as calculated discharge from Mannings equation): 

Q = K * SQRT(S) 

Where:
* S is a representative slope value for the downstream reach.
* K is obtained from the '''1d_ta_tables.csv'''

<li>Use the Elevation (Head) vs Discharge pairs to populate the HQ table.
</ol>

==Why is the maximum 1D water level higher than 2D water level?==
TUFLOW calculates storage at 1D nodes (including manholes) and calculates flux at pipe mid-sections. The storages at the manholes are saved in the format of ‘elevation vs nodal area’ tables, which can be reviewed in the .eof file: 
<ol>
[[File:1DpitHmax 1.png]] 
</ol>

The change in water volume in a manhole is calculated based on the inflow from the pit above (if there is one) and the inflow/outflow from the connected pipes. The change in water level is then calculated based on the change in volume and the nodal area at that elevation. 
<ol>
[[File:1DpitHmax 2.png | 300px ]] 
</ol>

TUFLOW extends the ‘elevation vs nodal area’ table by 5 m (the last 2 numbers in the table above), which allows for the calculation of pressurised pipe flow when a manhole is drowned. If the maximum 1D water level is higher than the DEM, that means the manhole was drowned and pressurised during the peak of the flood. 
<ol>
[[File:1DpitHmax 3.png | 300px ]] 
</ol>

==How to represent the hydraulic behavior at the outlet of a pipe, including headwalls and transitions to open channels? ==
In general, pipe outlet headwalls affect the exit loss at the pipe outlet, and this can be modeled by changing the 1d_nwk “ExitC_or_WSb” attribute - exit loss coefficient for outlet controlled flow. The default exit loss coefficient is 1.0, assuming there is no headwall. For more details on these factors, refer to the [https://docs.tuflow.com/classic-hpc/manual/latest/ TUFLOW Manual].
More guidance on applying entry/loss and contraction coefficients is available [[TUFLOW_1D_Channels_and_Hydraulic_Structures#What_entry.2Fexit_loss_and_contraction_coefficients_should_I_use_for_1D_culverts.3F | here]].

Once the water is in the 2D domain, headwalls can also affect the local flow field. The modelled hydraulic behavior depends on the model cell size. If the model has grid size smaller than the length of the headwall, thin breaklines or 2d_zsh tin can be added to represent the geometry in the 2D model. If the grid size is larger than the headwall, it only calculates ‘cell-averaged’ velocity including the impact of the headwall.

==Calculating velocity and bed shear stress at a pipe outlet==
The velocity and bed shear stress reported in the 2D domain at a pipe outlet depends on the model cell size. If the cell size is small enough and any outlets structures (i.e. wingwalls) are properly represented by the 2d cell/face elevations, TUFLOW can reasonably represent the flow field at the pipe outlet and estimate the bed shear stress base on the manning’s equation. However, be aware that TUFLOW solves 2D depth-averaged shallow water equation, so there is limitation of using it to estimate bed shear stress around complex structures or geometry that could experience vertical acceleration or re-circulation.

== How do I model blockage for irregular shaped culverts or channels? ==
For irregular shaped culverts and channels (1d_nwk "I" type), it is possible to model blockage by using the Skew (in degrees) attribute in the 1d_xs layer. This reduces the effective flow width of the cross section by rotating it relative to the direction of flow and can be used to approximate reduced capacity.

A skew angle of 0 means the cross section is aligned with the flow. As the angle increases, the effective width is reduced. For example, a skew of 60 degrees results results in a 50% reduction (as cos(60) = 0.5). While this does reduce the effective width of the structure, it isn’t expected to see the same reduction to conveyance as conveyance depends on the Manning’s n value, flow area and hydraulic radius.

== Do calculations differ if I specify multiple culverts using the Number_of attribute, even if the total dimensions are the same as a single culvert? ==
Yes. This can slightly affect the modelling results.

The key difference between a single wide culvert and multiple culverts with the same total width is the wetted perimeter. Multiple culverts have a slightly larger wetted perimeter due to the internal walls, which increases friction in downstream-controlled regimes. While friction losses are generally small compared to entry and exit losses, the impact on hydraulic behaviour is usually minor.

However, it’s important to model the configuration that best represents the actual structure when assessing culvert performance.

== How do I model a scruffy dome? ==
A scruffy dome is a type of dome inlet screen installed over a drop inlet, commonly used in stormwater systems. These inlets can be modelled in TUFLOW using a 1D Q type pit with a depth–discharge curve that reflects the hydraulic performance of the dome.

[https://www.ipwea-qnt.com/products-resources/qudm/ QUDM] recommends using the lesser of the weir flow or orifice flow equations to define inlet capacity (see Section 7.5.4). A blockage factor is also recommended to account for potential debris build-up during flood events (see Table 7.5.1 Provision for blockage at kerb inlets). This factor may vary depending on local guidelines, so regional standards should be consulted.

Once the rating curve is calculated, it can be added to the pit inlet database and linked to the Q type pit.
 
<ol>
[[File:How_do_I_model_a_scruffy_dome_in_TUFLOW.png|300x300px]] 
</ol>

== How can trash screens be represented? ==
A trash screen is a physical barrier, typically made from bars or mesh, placed at the inlet or outlet of a culvert, pipe, or channel to prevent debris from entering or exiting the structure.

Trash screens can be represented by applying additional head losses and blockage effects in accordance with [https://www.ipwea-qnt.com/products-resources/qudm/ QUDM] guidance. The approach depends on the location of the screen and the degree of blockage.

Possible use cases include:

* Where the screen is attached directly to the culvert inlet or outlet, the losses are applied to the culvert itself.
* Where the screen is located upstream of a culvert inlet or downstream of a culvert outlet, it can be modelled as a short zero length CF, IF, or RF channel before (upstream) or after (downstream) the culvert.
* In all cases, the net flow area should be reduced to account for the bars and any debris blockage, either by adjusting the flow area directly or by using the pBlockage attribute.

For example, the image below could be represented as a rectangular shaped screen located upstream or downstream of a culvert, modelled as a short zero length RF (rectangular culvert with fixed losses) channel using a 1d_nwk GIS layer.
<ol>
[[File:Trash_screen_schematic.png|alt=|300x300px]]
</ol>

== How can fishway baffles in a culvert be modelled? ==
UNDER CONSTRUCTION

Fishway baffles are barriers inside a culvert that slow the water and help fish swim through.

These approaches could be used to represent them inside a model:

* Short culvert with smaller opening.
** A short culvert section is added with its bottom and top levels set to match the fish baffle. The width is set to the total width of the fish passage gaps. This represents the reduced space for water to pass through.

* Two culverts in a row.
** Two culverts are connected in series. The first is the normal culvert, and the second is a very short culvert with a smaller opening sized to the fish baffle gaps.

For both options, sensitivity testing is recommended because at low flows the way the culvert is defined can change the results. These methods do not include turbulence or detailed 3D water movement around the baffles. If accurate fish passage flows are required, a CFD 3D model can be used to determine the flows and those flows can then be applied using a 1D Q type channel.
<ol>
[[File:Rectangular culvert baffled.png|alt=|300x300px]]
</ol>

== How can a flow splitter with a nib wall be modelled? ==
UNDER CONSTRUCTION

A flow splitter with a nib wall can be modelled in TUFLOW by introducing a 1D weir between the manhole and the relevant outlet pipe. The weir crest elevation should be set equal to the height of the nib wall within the chamber. This setup allows low flows to discharge through one pipe, while higher flows overtop the weir into a secondary outlet, replicating the behaviour of a physical flow splitter.

The main outlet pipes are connected through a manhole, which automatically applies appropriate entry and exit losses. For outlets that do not connect further downstream, entry and exit losses should be manually defined in the attributes.

Refer to the schematic example below for this configuration.

 

{{Tips Navigation
|uplink=[[ TUFLOW_Modelling_Guidance | Back to TUFLOW Modelling Guidance]]
}}

HPC Adaptive Timestepping

2025-11-10T05:45:12Z

Abrar.Alttahir: /* HPC 1D Timestep */

=Introduction=

The HPC solver, by default, uses adaptive timestepping to progress through the simulation. The timestep is adjusted so that it complies with the mathematical stability criteria of a 2D SWE explicit solution. In contrast, TUFLOW Classic, being an implicit solution, is not numerically bound by these criteria, and can progress through the simulation using a fixed timestep. Due to the underlying solution scheme, HPC typically uses a smaller timestep than Classic. the following sections outline how TUFLOW HPC automatically selects its adaptive timestep.

=HPC 2D Timestep=
TUFLOW HPC uses adaptive timestepping to maintain stability. There are three primary processes that determine the maximum timestep that an explicit solution to the Shallow Water Equations can use:
* Courant Number, Nu 
* Wave Celerity Number, Nc 
* Diffusion Number, Nd 

{|class="wikitable" style="text-align: center;"
!colspan="4" style="background-color:#005581; height:50px; font-weight:bold; color:white;"|HPC 2D Adaptive Timestep Controlling Numbers
|-
|style="width: 11%; height:50px;" | Control Number||style="width: 40%;" | Description||style="width: 18%;" | Expression||style="width: 9%;" | Control Number Limit
|-
|style="height:80px;" | Courant Number (Nu)
|This condition ensures that water entering one side of a 2D cell does not pass through the other side within one timestep. For this to be satisfied, the product of the water velocity (𝑢) and model timestep (∆𝑡) must be less than the cell size (∆𝑥).
|[[FILE: Courant Number Equation.PNG |250px]]
|≤1.0
|-
|style="height:110px;" | The Shallow Wave Celerity Number (Nc)
|This numerical condition relates to the shallow water wave celerity (wave speed) and is derived from the fluid flow equations to represent long waves (i.e. wave length is substantially longer than the water depth). The product of the model timestep (∆𝑡) and the long wave speed (square root of the gravity (g) and water depth (h)) must be less than the cell size (∆𝑥), for the condition to be satisfied.
|[[FILE: SWC Number Equation.PNG |250px]]
|≤1.0
|-
|style="height:80px;" | Diffusion Number (Nd)
|This numerical condition relates to the sub-grid scale eddy viscosity term which causes diffusion of momentum. To maintain stability the product of the eddy viscosity coefficient (ν_T) and the timestep (∆𝑡) divided by the square of the grid spacing (∆𝑥2) must remain below 0.3. Models controlled by the diffusion number tend to require a timestep significantly smaller than those controlled by the shallow wave celerity or courant numbers. If you find your model is predominantly diffusion controlled it may be that equivalent solution accuracy can be achieved by selecting a larger cell size. This is worth testing, as it will most likely increase the simulation speed with no loss of result fidelity.
|[[FILE: Diffusion Number Equation.PNG |250px]]
|≤0.3
|}

In general terms:
* Courant number relates to velocity relative to the cell size. High velocities will trigger this as the control.
* Celerity Control number relates to water depth relative to cell size. Energy can pass through deeper water faster than shallow water, as such deep water will trigger this control.
* Diffusion control relates diffusion of momentum relating to the sub grid viscosity. Small cells in deep water will trigger this one.
TUFLOW will use the highest timestep possible without exceeding the limits associated with each of the control numbers. 
The method TUFLOW HPC uses to calculate a timestep and achieve unconditional stability is as follows:
* The <tt>Timestep </tt> command included in the TUFLOW Control File (TCF) is only used for the first calculation timestep. The specified value should be consistent with what would be appropriate for a TUFLOW Classic model (i.e. 1/2 to 1/5 the 2D cell size). Internally, TUFLOW HPC divides this value by 10 to apply a value that is suitable for an explicit solution scheme. All subsequent calculations are completed using the adaptive timestep approach outlined in the following bullet points.
* The HPC timestep is calculated using the hydraulic conditions from the end of the previous timestep.
* If the hydraulic conditions have changed significantly it is possible for one or more of the Nu, Nc, Nd control number criteria to be violated at one or more locations within the model. For example, a sudden change in rainfall from one timestep to the next (which occurs with stepped rainfall boundaries) would potentially cause a violation. The HPC solver, by default, treats a 20% exceedance of a control number as a violation and will implement a repeat timestep feature. HPC uses a repeat timestep feature to maintain unconditional stability. The repeat timestep feature involves retaining the complete hydraulic solution from the previous (good) timestep. Should a control number anywhere within the model be exceeded by more than 20%, the solution reverts to the retained timestep, the timestep is reduced and then repeated.
* Each timestep is also tested for the occurrence of NaNs. A NaN is “Not a Number” and occurs due to undefined mathematical calculations such as a divide by zero or square root of a negative number. The occurrence of a NaN is also indicative of a sudden instability. Should a NaN occur, the repeat timestep feature is implemented. 
* Should a timestep need to be repeated more than ten times consecutively, the solution stops.
* The simulation will also stop if the default minimum permissible timestep of 0.1 seconds has been reached. This value can be manually adjusted using <tt>Timestep Minimum</tt>. 
Repeated timesteps are displayed to the Console Window and the number of them for a time interval are provided in the nRS_NaNs and nRS_HCNs columns in the _HPC.csv file output in the results folder. They are also reported to the _messages layer. 
Repeated timesteps are an indication the 2D HPC solution is numerically “on-the-edge”. Models that have a high number of repeated timesteps should be sensitivity tested by reducing the control number limits using <tt>Control Number Factor</tt>. For example, repeat the simulation using <tt>Control Number Factor</tt><tt>==</tt><tt> 0.8 </tt> and compare the results. If there are acceptably immeasurable changes in the results, then running at the default control number limits can be considered satisfactory.

''NOTE: Models that require an excessively small 2D timestep to remain stable are symptomatic of models with input topography or boundary condition errors. Please refer to [Tutorial_Module03 | Tutorial Module 03] for discussion on this topic.''

=HPC 1D Timestep=
HPC links with the 1D solver, ESTRY. When run with HPC (instead of Classic) ESTRY has been reconfigured to automatically act as an adaptive/varying timestep solution, and can step at different multiples of steps to the HPC 2D solver. The ESTRY 1D <tt>Timestep </tt> command for a HPC 1D/2D linked model is the maximum limiting timestep the 1D solver can use, and the 1D and 2D solutions are synchronised based on the size of the "maximum 1D timestep" and the "target 2D timestep":
* If target 2D timestep = maximum 1D timestep: No adjustment is made.
* If target 2D timestep > maximum 1D timestep: the 1D proceeds in two or more steps eventually synchronising with the target 2D timestep.
* If target 2D timestep < Maximum 1D timestep: the 2D timestep is reduced below the target value to synchronise with 1D at a multiple of the 2D timesteps.
Where there is not a one to one synchronisation of the 1D and 2D timesteps, a usually negligible mass error may occur and can be checked by reviewing the CME% values shown on the Console Window, the .tlf file or the _MB.csv file in the same manner as Classic.
 
[[File:TUFLOWHPC1D2DTimestepSync2.png]]

=HPC Timestep Synchronisation=
HPC uses an adaptive timestep derived from the hydraulic conditions during a simulation. This timestepping approach creates a situation where the computational timestep does not always align exactly with the specified map and time series result output intervals. To account for this TUFLOW HPC makes a timestep adjustments to synchronize the computational timestep with output reporting times. When this occurs the minimum timestep (dt) reported in hpc.dt.csv will report a reduced timestep. This adjustment doesn’t have any effect on the hydraulic calculations, and as such, is not reported in the hpc.tlf or shown in the dt map output. The "dtStar" column in the hpc.dt.csv file also reports the calculated 2D target timestep without any result output synchronisation applied. If the hpc.dt.csv file is being used to quality check the health of a simulation, "dtStar" should be used for the analysis, not "dt". 
 
[[File:dt_sync.png|500px]] 
 

=HPC Quadtree Adaptive Timestepping=
All the above is applicable to the HPC with Quadtree simulations. In addition the Quadtree solver uses a single adaptive timestep for the entire model at any instance in time. Different timesteps are not applied to the respective different resolution nest levels. If a small cell resolution is nested down in water that is deeper than the cell face length, the Diffusion control number would be the critical limiter that is setting the timestep and the model will run slower than expected. This can be confirmed by reviewing the [[HPC_Model_Review#HPC_dt_Time_Series_Output_.28.2A.hpc.dt.csv.29 | hpc.dt.csv file]] information. 
 
{{Tips Navigation
|uplink=[[ HPC_Modelling_Guidance | Back to HPC Modelling Guidance]]
}}

File:TUFLOWHPC1D2DTimestepSync2.png

2025-11-10T05:44:27Z

Abrar.Alttahir:

Updated 1D-2D timestep synchronisation theory figure for TUFLOW HPC, showing the correct relationship between 1D and 2D timesteps as implemented in version 2025.2. Replaces the previous outdated figure on the wiki.