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 | Minimum 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:L4:1 or #SBATCH --gres=gpu:V100:1 or #SBATCH --gres=gpu:V100S:1 #SBATCH --time=4:00:00 |
| 2 | 64 | 745GB | AMD Genoa | L4 | 4 | 24GB | 8.9 | 11.8 | buyin partition | |
| 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 |
Software
Sapelo2 has several tools for GPU programming and many CUDA-enabled applications. For example:
1. NVIDIA CUDA toolkit
Several versions of the CUDA toolkit are available. Please see our CUDA page.
2. cuDNN
The NVIDIA CUDA Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks.
To see all modules of cuDNN installed on Sapelo2, please use the command
ml spider cuDNN
3. NCCL
The NVIDIA Collective Communications Library (NCCL) implements multi-GPU and multi-node collective communication
primitives that are performance optimized for NVIDIA GPUs.
To see all modules of cuDNN installed on Sapelo2, please use the command
ml spider NCCL
4. OpenACC
Using the NVIDIA HPC SDK compiler suite, provided by the NVHPC module on Sapelo2, 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 compilers installed on Sapelo2, please see Code Compilation on Sapelo2.
5. CUDA-enabled applications
CUDA-enabled applications typically have a version suffix in the module name to indicate the version of CUDA that they were built with. Some examples of applications built with CUDA-12.1.1 that support up to compute capability 9.0 include:
- GROMACS/2023.3-foss-2023a-CUDA-12.1.1-PLUMED-2.9.0
- GROMACS/2023.4-foss-2023a-CUDA-12.1.1
- PyTorch/2.1.2-foss-2023a-CUDA-12.1.1
Running Jobs
For information on how to run GPU jobs on Sapelo2, please refer to Running Jobs on Sapelo2.
Important notes:
1. If a job requests
#SBATCH --partition=gpu_p #SBATCH --gres=gpu:1
then it can get allocated any GPU device type (i.e. P100, A100, L4, or H100). If you opt for requesting a GPU device without specifying its type, please make sure that the application or code you are running works for all device types.
2. If the application that you are running uses an older version of CUDA, for example CUDA/11.4.1 or CUDA/11.7.0, please request an explicit GPU device that supports the CUDA version. For example, request an A100 device with
#SBATCH --partition=gpu_p #SBATCH --gres=gpu:A100:1