Two BRCF research pods have NVIDIA GPU servers; however their use is restricted to the groups who own those pods.
Servers
Hopefog pod
hfogcomp04.ccbb.utexas.edu compute server on the Hopefog pod (Ellington/Marcotte):
- Dell PowerEdge R750XA
- dual 24-core/48-thread CPUs (48 cores, 96 hyperthreads total)
- 512 GB RAM
- 2 NVIDIA Ampere A100 GPUs w/32GB onboard RAM each
Wilke pod
wilkcomp03.ccbb.utexas.edu compute server on the Wilke pod:
- GIGABYTE MC62-G40-00 workstation
- AMD Ryzen 5975WX CPU (32 cores, 64 hyperthreads total)
- 512 GB RAM
- 1 NVIDIA RTX 6000 GPU
Resources
Tests
Use nvidia-smi to verify access to the server's GPUs
Two Python scripts are located in /stor/scratch/GPU_info that can be used to ensure you have access to the server's GPUs. Run them from the command line using time to compare the run times.
- Tensor Flow
- time ( python3 /stor/scratch/GPU_info/tensorflow_example.py )
- should take 30s or less with GPU, > 1 minute with CPUs only
- this is a simple test, and on CPU-only servers multiple cores are used but only 1 GPU, one reason why the times are not more different
- time ( python3 /stor/scratch/GPU_info/tensorflow_example.py )
- PyTorch
- time ( python3 /stor/scratch/GPU_info/pytorch_example.py )
- takes ~30s or less to complete on wilkcomp03
- takes ~1m to complete on hfogcomp04.
- time ( python3 /stor/scratch/GPU_info/pytorch_example.py )
CUDA
hfogcomp04 has both CUDA 11 and CUDA 12 installed. wilkcomp03 has only CUDA 12, and needs CUDA 11 installed.
AlphaFold
Native AlphaFold for NVIDIA CPUs has not yet been installed; however AlphaFold databases are available in /stor/scratch/AlphaFold.