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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Both hfogcomp04 and wilkcomp03 have both CUDA 11.8 and CUDA 12.x installed, under version-specific subdirectories of /usr/local.
To ensure CUDA 11 is made active:
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export CUDA_HOME=/usr/local/cuda-12 export PATH=$CUDA_HOME/bin:$PATH |
CUDA drivers are installed under /usr/lib/x86_64-linux-gnu/. To see what version is currently installed:
ls /usr/lib/x86_64-linux-gnu/libnvidia-gl*.
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.
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