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Two BRCF research pods have NVIDIA GPU servers; however their use is restricted to the groups who own those pods. 

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The AlphaFold protein structure solving software is available on all AMD GPU servers. The /stor/scratch/AlphaFold directory has the large required database, under the data.3 sub-directory. There is also an AMD example script /stor/scratch/AlphaFold/alphafold_example_amd.shand an alphafold_example_nvidia.sh script if the POD also has NVIDIA GPUs, (e.g. the Hopefog pod). Interestingly, our timing tests indicate that AlphaFold performance is quite similar on all the AMD and NVIDIA GPU servers.

TensorFlow and PyTorch examples

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If GPUs are available and accessible, the output generated will indicate they are being used.

Resources

Command-line diagnostics

Use nvidia-smi to verify access to the server's GPUs

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Both hfogcomp04 and wilkcomp03 have both CUDA 11.8 and CUDA 12.x installed.

To ensure CUDA 11 is made active:

export CUDA_HOME=/usr/local/cuda-11.8
export PATH=$CUDA_HOME/bin:$PATH

To ensure CUDA 12 is made active:

export CUDA_HOME=/usr/local/cuda-12
export PATH=$CUDA_HOME/bin:$PATH

Sharing resources

Since there's no batch system on BRCF POD compute servers, it is important for users to monitor their resource usage and that of other users in order to share resources appropriately.

• Use top to monitor running tasks (or top -i to exclude idle processes)
  • commands while top is running include:
  • M - sort task list by memory usage
  • P - sort task list by processor usage
  • N - sort task list by process ID (PID)
  • T - sort task list by run time
  • 1 - show usage of each individual hyperthread
    • they're called "CPUs" but are really hyperthreads
    • this list can be long; non-interactive mpstat may be preferred
• Use mpstat to monitor overall CPU usage
  • mpstat -P ALL to see usage for all hyperthreads
  • mpstat -P 0 to see specific hyperthread usage
• Use free -g to monitor overall RAM memory and swap space usage (in GB)
• Use nvidia-smi to  monitorGPU usage