Both sides previous revision
Previous revision
Next revision
|
Previous revision
Next revision
Both sides next revision
|
cluster:182 [2019/08/12 14:08] hmeij07 [P100 vs RTX 6000 & T4] |
cluster:182 [2019/08/12 14:39] hmeij07 [the DPP] |
[[http://www.microway.com|Microway]] | [[http://www.microway.com|Microway]] |
| |
First though...mixed precision calculations are on the rise, driven by Deep Learning. Obviously the researcher needs to evaluated if veering away from double precision calculations is scientifically sound. [[https://www.hpcwire.com/2019/08/05/llnl-purdue-researchers-harness-gpu-mixed-precision-for-accuracy-performance-tradeoff/|GPUmixer: harness gpu mixed precision]] | First though...mixed precision calculations are on the rise, driven by Deep Learning. Obviously the researcher needs to evaluate if veering away from double precision calculations is scientifically sound. [[https://www.hpcwire.com/2019/08/05/llnl-purdue-researchers-harness-gpu-mixed-precision-for-accuracy-performance-tradeoff/|GPUmixer: harness gpu mixed precision]] |
| |
==== the DPP ==== | ==== the DPP ==== |
The Double Precision Problem. | The Double Precision Problem. |
| |
[[http://https://www.microway.com/knowledge-center-articles/comparison-of-nvidia-geforce-gpus-and-nvidia-tesla-gpus/|Comparison of Nvidia, GeForce GPUs and Nvidia Tesla GPUs]] | [[https://www.microway.com/knowledge-center-articles/comparison-of-nvidia-geforce-gpus-and-nvidia-tesla-gpus/|Comparison of Nvidia, GeForce GPUs and Nvidia Tesla GPUs]] |
| |
[[https://en.wikipedia.org/wiki/List_of_Nvidia_graphics_processing_units|List of Nvidia Graphics Processing Units]] | [[https://en.wikipedia.org/wiki/List_of_Nvidia_graphics_processing_units|List of Nvidia Graphics Processing Units]] |
| |
"Every GPU with SM 1.3 (Tesla/GTX2xx) or better has hardware double-precision support. Starting with the Fermi architecture, Quadro and Tesla variants have better double-precision support than consumer Ge Force models." So I'm utterly confused by this outcome. The P100 is best at double precision (fp64), the RXT6000 is modest and the T4 actually has no specs regarding fp64. Running a colloid example in Lammps compiled for these GPUs with DOUBLE_DOUBLE, all three models obtain the same result in 500,000 loops. | "Every GPU with SM 1.3 (Tesla/GTX2xx) or better has hardware double-precision support. Starting with the Fermi architecture, Quadro and Tesla variants have better double-precision support than consumer Ge Force models." So I'm utterly confused by this outcome. The P100 is best at double precision (FP64), the RXT6000 is modest and the T4 actually has no specs regarding FP64. Nvidia does not publish any data on FP64 for T4 and certain RTX models. But running a colloid example in Lammps compiled for these GPUs with DOUBLE_DOUBLE, all three models obtain the same result in 500,000 loops. |
| |
The explanation was found [[https://www.microway.com/hpc-tech-tips/nvidia-turing-tesla-t4-hpc-performance-benchmarks/|T4 benchmarks fp64 and fp32]]. The T4 can do double precision if needed but it's strength is mixed and single precision. | The explanation was found [[https://www.microway.com/hpc-tech-tips/nvidia-turing-tesla-t4-hpc-performance-benchmarks/|T4 benchmarks fp64 and fp32]]. The T4 can do double precision if needed but it's strength is mixed and single precision. |
| |
</code> | </code> |
| |
| ==== Amber ==== |
| |
| ^ ^ ^ P100[1] ^ P100[4] ^ RTX[1] ^ T4[1] ^ T4[4] ^ Notes ^ |
| |
| |
\\ | \\ |
**[[cluster:0|Back]]** | **[[cluster:0|Back]]** |