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
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CUDA
Both hfogcomp04 and wilkcomp03 have both CUDA 11.8 and CUDA 12.x installed, under version-specific subdirectories of /usr/local.
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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*.
Command-line diagnostics
Use nvidia-smi to verify access to the server's GPUs and to monitor GPU usage.
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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