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.
Note that our
Resources
CUDA
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 |
Neither version is specified by default, and some (but not all) programs rely on these environment variables. So you should activate one or the other before running software that uses GPUs.
After setting these environment variables, type nvcc --version to ensure you have access to the desired version.
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*. See https://saturncloud.io/blog/where-did-cuda-get-installed-in-my-computer/.
Command-line diagnostics
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