NVIDIA GPU servers

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
• PyTorch
  
  • time ( python3 /stor/scratch/GPU_info/pytorch_example.py )
    • takes ~30s or less to complete on wilkcomp03
    • takes ~1m to complete on hfogcomp04.

CUDA

These servers have both CUDA 11 and CUDA 12 installed