GPUs on HPC
| GPU | Full Name | Year Launched | Memory | # of Tensor Cores |
|---|---|---|---|---|
| A100 | NVIDIA A100 | 2020 | 40GB or 80GB | 432 (3rd gen) |
| A6000 | NVIDIA RTX A6000 | 2020 | 48GB | 336 (3rd gen) |
| A40 | NVIDIA A40 | 2020 | 48GB | 336 (3rd gen) |
| RTX3090 | NVIDIA GeForce RTX 3090 | 2020 | 24GB | 328 (3rd gen) |
| RTX2080Ti | NVIDIA GeForce RTX 2080 Ti | 2018 | 11GB | 544 (2nd gen) |
| V100 | NVIDIA V100 | 2018 | 32GB | 640 (1st gen) |
UVA-NVIDIA DGX BasePOD
- 10 DGX A100 nodes
- 8 NVIDIA A100 GPUs.
- 80 GB GPU memory options.
- Dual AMD EPYC™️; nodes: Series 7742 CPUs, 128 total cores, 2.25 GHz (base), 3.4 GHz (max boost).
- 2 TB of system memory.
- Two 1.92 TB M.2 NVMe drives for DGX OS, eight 3.84 TB U.2 NVMe drives forstorage/cache.
- Advanced Features:
- NVLink for fast multi-GPU communication
- GPUDirect RDMA Peer Memory for fast multi-node multi-GPU communication
- GPUDirect Storage with 200 TB IBM ESS3200 (NVMe) SpectrumScale storage array
- Ideal Scenarios:
- Job needs multiple GPUs on a single node or multi node
- Job (single or multi-GPU) is I/O intensive
- Job (single or multi-GPU) requires more than 40GB of GPU memory
Note: The POD is good if you need multiple GPUs and very fast computation.
GPU access on UVA HPC
When you request memory for UVA HPC, that is CPU memory.
If you request a GPU, you will receive all of the GPU memory.
- Choose “GPU” or “Interactive” as the HPC Partition in OOD
- Optionally, choose GPU type and number of GPUs
- POD nodes are contained in the gpu partition with a specific Slurm constraint.
- Slurm script:
#SBATCH -p gpu
#SBATCH --gres=gpu:a100:X # X number of GPUs
#SBATCH -C gpupod
- Open OnDemand
--constraint=gpupod
Note: Only one person can be using a GPU at a time.