Hardware Benchmarking Topic Single Precision VS Double Precision
Page Under Construction
Introduction
Both TUFLOW Classic and TUFLOW HPC can run using either a single precision (SP) or double precision (DP). When storing floating point values on a computer, a certain number of bytes per value is needed. Single precision numbers use 4 bytes and double precision numbers use 8 bytes. This will yield about 7 digits of precision for single precision and 16 digits for double.
This page discuss the relative difference in performance of the SP and DP versions of TUFLOW. This includes comparisons for TUFLOW Classic, TUFLOW HPC on CPU hardware and TUFLOW HPC on GPU hardware.
Double precision calculations take slightly longer and require more memory for the field data. The memory requirement of DP is almost twice that of SP. Therefore, if the results of a model run in both SP and DP versions of TUFLOW prove to be similar, the SP version of TUFLOW is recommended as it will be slightly faster and will enable larger models to be run within available CPU/GPU memory.
Note Single precision calculations are also referred to as FP32 (32 bit floating point) and double precision as FP64 (64 bit floating point) calculations. This seems to be a more common terminology in GPU benchmarks.
Benchmark Model
The benchmark model is based on a “challenge” issued prior to the 2012 Flood Managers Association (FMA) Conference in Sacramento, USA. There is more information on the model setup and purpose in the FMA challenge model introduction.
This hardware benchmark is based on the second challenge which involves a coastal river in flood with two ocean outlets. The model has been modified slightly (mainly in terms of the outputs). It is setup to use both the TUFLOW Classic (CPU) and TUFLOW HPC (on both CPU and GPU hardware) with 20m cell size and 181,981 2D cells.
TUFLOW Classic
The table below has runtimes for the benchmark model at 20m cell size. The same model has been run for both the SP and DP versions of TUFLOW using the Classic solution scheme on CPU hardware. This same test has been performed on a number of CPU chips.
| CPU | SP Runtime (mins) | DP Runtime (mins) | % Change |
|---|---|---|---|
| AMD Ryzen Threadripper 2990WX 32-Core Processor | 65.8 | 80.3 | 22.0 |
| Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz | 71.7 | 87.4 | 21.9 |
| Intel(R) Core(TM) i7-6800K CPU @ 3.40 GHz | 90.0 | 119.1 | 32.3 |
| Intel(R) Core(TM) i7-6900K CPU @ 3.20 GHz | 90.5 | 109.3 | 20.7 |
| Intel(R) Core(TM) i7-4790K CPU @ 4.00 GHz | 91.3 | 115.2 | 26.2 |
| Intel(R) Core(TM) i7-5960X CPU @ 3.00 GHz | 101.9 | 128.8 | 26.4 |
| Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz | 121.4 | 158.1 | 30.2 |
| Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40 GHz | 158.0 | 127.2 | 24.2 |
| Intel(R) Xeon(R) CPU X5680 @ 3.33 GHz | 162.1 | 207.6 | 28.1 |
TUFLOW HPC on CPU hardware
The table below has runtimes for the benchmark model at 20m cell size. The same model has been run for both the SP and DP versions of TUFLOW using the HPC solution scheme on CPU hardware. This same test has been performed on a number of CPU chips.
Note The GPU code has been compiled for CPU execution so users can trial the solver without access to an NVidia GPU if necessary, but the solver has been first and foremost design for Highly Parallel Compute on GPU hardware.
| CPU | SP Runtime (mins) | DP Runtime (mins) | % Change |
|---|---|---|---|
| Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz | 216.8 | 230.9 | 6.5 |
| Intel(R) Core(TM) i7-4790K CPU @ 4.00 GHz | 221.8 | 254.3 | 14.7 |
| Intel(R) Core(TM) i7-5960X CPU @ 3.00 GHz | 236.9 | 260.3 | 9.9 |
| Intel(R) Core(TM) i7-6800K CPU @ 3.40 GHz | 278.3 | 291.4 | 4.7 |
| AMD Ryzen Threadripper 2990WX 32-Core Processor | 278.8 | 347.7 | 24.7 |
| Intel(R) Core(TM) i7-6900K CPU @ 3.20 GHz | 286.0 | 328.9 | 15.0 |
| Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz | 298.2 | 322.6 | 8.2 |
| Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40 GHz | 307.2 | 350.6 | 12.4 |
| Intel(R) Xeon(R) CPU X5680 @ 3.33 GHz | 404.0 | 466.4 | 15.5 |
TUFLOW HPC on GPU hardware
For GPU devices, the quoted performance of GPU devices can be very different for single and double precision calculations. The table below has runtimes for the benchmark model at 20m cell size. The same model has been run for both the SP and DP versions of TUFLOW using the HPC solution scheme on GPU hardware. This same test has been performed on a number of different GPU cards.
| GPU Card | SP Runtime (mins) | DP Runtime (mins) | % Change |
|---|---|---|---|
| NVIDIA TITAN Xp | 5.7 | 10.6 | 87.6 |
| NVIDIA GeForce RTX 2080 SUPER | 7.0 | 14.0 | 100.5 |
| NVIDIA GeForce RTX 2080 | 7.6 | 16.1 | 111.4 |
| NVIDIA GeForce RTX 2070 | 8.9 | 18.4 | 107.3 |
| NVIDIA GeForce GTX 1080 Ti | 9.4 | 14.6 | 55.4 |
| NVIDIA GeForce GTX 1080 | 11.3 | 18.3 | 61.8 |
| NVIDIA Quadro RTX 4000 | 17.6 | 18.2 | 3.4 |
| NVIDIA GeForce GTX 980 | 17.7 | 29.8 | 68.0 |
| NVIDIA GeForce GTX 750 Ti | 28.6 | 72.8 | 60.8 |
| NVIDIA GeForce 940MX | 71.3 | 156.2 | 118.9 |
| NVIDIA GeForce 840M | 89.2 | 180.2 | 101.9 |
Conclusion
Running TUFLOW Classic (CPU hardware only) is consistently giving at least 20% difference between single and double precision. It is recommended to use double precision for TUFLOW Classic models for all rain on grid models and for models with elevation over 100 [m or ft]. This may become apparent if high mass balance values are experienced when the model is simulated using single precision.
The calculation method in TUFLOW HPC uses the depth as its conserved variable in the explicit scheme, unlike TUFLOW Classic that uses water level due to its implicit scheme. This means that precision issues associated with applying a very small rainfall to a high elevation are not applicable in HPC. Unless testing shows otherwise, single precision version of TUFLOW should be used for all HPC simulations. When TUFLOW HPC is used on CPU hardware the differences between single and double precision are ranging from 5% to 25% depending on the processor specifications.
Running TUFLOW HPC on GPU hardware shows even more significant differences between single and double precision.
The precision solver that is required for running TUFLOW on GPU hardware will determine the type of GPU card that is best suited for the 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.
Note Predicting how certain machine will perform and estimating runtime based on the hardware specification isn't possible as the TUFLOW code is very complex.