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:
** In progress **
Resources
Tests
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
- time (python3 /stor/scratch/GPU_info/tensorflow_example.py )
- PyTorch – **not yet working**
- time (python3 /stor/scratch/GPU_info/pytorch_example.py )
Note that 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.
CUDA
These servers have both CUDA 11 and CUDA 12 installed