GPU
GPU Computing on Sapelo2
Hardware
For a description of the Graphics Processing Units (GPU) device specifications, please see GPU Hardware.
The following table summarizes the GPU devices available on sapelo2:
| Number of nodes | CPU cores per node | Host memory per node | CPU processor | GPU model | GPU devices per node | Device memory | GPU compute capability | CUDA version | Partition Name | Notes |
|---|---|---|---|---|---|---|---|---|---|---|
| 10 | 128 | 1TB | Intel Sapphire Rapids | H100 | 4 | 80GB | 9.0 | >=11.8 | gpu_p, gpu_30d_p | Need to request --gres=gpu:H100, e.g.,
#SBATCH --partition=gpu_p #SBATCH --gres=gpu:H100:1 #SBATCH --time=7-00:00:00 |
| 14 | 64 | 1TB | AMD Milan | A100 | 4 | 80GB | 8.0 | >=11.0 | gpu_p, gpu_30d_p | Need to request --gres=gpu:A100, e.g.,
#SBATCH --partition=gpu_p #SBATCH --gres=gpu:A100:1 #SBATCH --time=7-00:00:00 |
| 11 | 128 | 745GB | AMD Genoa | L4 | 4 | 24GB | 8.9 | >=11.8 | gpu_p, gpu_30d_p | Need to request --gres=gpu:L4, e.g.,
#SBATCH --partition=gpu_p #SBATCH --gres=gpu:L4:1 #SBATCH --time=7-00:00:00 |
| 2 | 32 | 192GB | Intel Skylake | P100 | 1 | 16GB | 6.0 | >=8.0 | gpu_p, gpu_30d_p | Need to request --gres=gpu:P100, e.g.,
#SBATCH --partition=gpu_p #SBATCH --gres=gpu:P100:1 #SBATCH --time=7-00:00:00 |
| 1 | 64 | 1TB | AMD Milan | A100 | 4 | 80GB | 8.0 | >=11.0 | buyin partition | Available on batch for all users up to 4 hours, e.g.,
#SBATCH --partition=batch #SBATCH --gres=gpu:A100:1 or #SBATCH --gres=gpu:V100:1 or #SBATCH --gres=gpu:V100S:1 or #SBATCH --gres=gpu:L4:1 #SBATCH --time=4:00:00 |
| 2 | 28 | 192GB | Intel Skylake | V100 | 1 | 16GB | 7.0 | >=9.0 | buyin partition | |
| 2 | 32 | 192GB | Intel Skylake | V100 | 1 | 16GB | 7.0 | >=9.0 | buyin partition | |
| 2 | 32 | 384GB | Intel Skylake | V100 | 1 | 32GB | 7.0 | >=9.0 | buyin partition | |
| 2 | 64 | 128GB | AMD Naples | V100 | 2 | 32GB | 7.0 | >=9.0 | buyin partition | |
| 1 | 64 | 128GB | AMD Naples | V100 | 1 | 32GB | 7.0 | >=9.0 | buyin partition | |
| 4 | 64 | 128GB | AMD Rome | V100S | 1 | 32GB | 7.0 | >=9.0 | buyin partition | |
| 2 | 64 | 745GB | AMD Genoa | L4 | 4 | 24GB | 8.9 | >=11.8 | buyin partition |
Software
Sapelo2 has the following tools for programming for GPUs:
1. NVIDIA CUDA toolkit
Several versions of the CUDA toolkit are available. Please see our CUDA page.
2. PGI/CUDA compilers
The PGI compilers available on Sapelo2 support GPU acceleration, including Fortran/CUDA.
For more information on the GPU support of PGI compilers, please visit the PGI website http://www.pgroup.com/resources/cudafortran.htm
For information on versions of PGI compilers installed on Sapelo2, please see Code Compilation on Sapelo2.
3. OpenACC
Using the NVIDIA HPC SDK compiler suite or the old PGI Accelerator compilers, programmers can accelerate applications on x64+accelerator platforms by adding OpenACC compiler directives to Fortran and C programs and then recompiling with appropriate compiler options. Please see https://developer.nvidia.com/hpc-sdk and http://www.pgroup.com/resources/accel.htm
OpenACC is also supported by GNU compilers, especially the latest versions, e.g. GNU 7.2.0, installed on Sapelo2. For more information on OpenACC support by GNU compilers, please refer to https://gcc.gnu.org/wiki/OpenACC
For information on versions of GNU compilers installed on Sapelo2, please see Code Compilation on Sapelo2.
Running Jobs
For information on how to run GPU jobs on Sapelo2, please refer to Running Jobs on Sapelo2.