Hardware Selection Advice: Difference between revisions
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=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.
In the sections below you will find more detailed advice on GPU and CPU but generally:<br>
* 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 <u>[[TUFLOW Simulation Speed | TUFLOW Simulation Speed]]</u>). ▼
▲* 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).
* 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. <br>
For information on minimum and recommended system requirement, see <u>[[System_Requirements | System Requirements]]</u>.
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=The TUFLOW Software Suite=
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*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.<br>
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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 <u>[https://developer.nvidia.com/cuda-gpus NVIDIA website]</u>.
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*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===
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===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.
===CUDA Cores, GPU Clock speed, and FLOPs ===
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===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 <u>[[
External videocard benchmark websites can be used to compare GPU cards, for example, <u>[https://www.videocardbenchmark.net/high_end_gpus.html PassMark Software - Video Card (GPU) Benchmarks]</u> 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.
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===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 <u>[[
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=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.<br>
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