Two BRCF research pods have NVIDIA GPU servers; however their use is restricted to the groups who own those pods.
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- Dell PowerEdge R750XA
- dual 24-core/48-thread CPUs (48 cores, 96 hyperthreads total)
- 512 GB system RAM
- 2 NVIDIA Ampere A100 GPUs w/32GB 80GB onboard RAM each
hfogcomp05.ccbb.utexas.edu
- GIGABYTE MC62-G40-00
- 32-core/64-thread AMD Ryzen CPU
- 512 GB RAM
- 4 NVIDIA RTX 6000 Ada GPUs, 48G RAM each
Wilke pod
wilkcomp03.ccbb.utexas.edu compute server on the Wilke pod:
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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
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Two Python example scripts are located in /stor/scratch/GPU_info that can be used to ensure you have access to the server's GPUs from TensorFlow or PyTorch. Run them from the command line using time to compare the run times.
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Note that our system-wide CUDA-enabled TensorFlow and PyTorch versions are only available in the default Python 3 command-line environment (e.g. python3 or python3.8 on the command line). They are not yet available in the global JupyterHub environment that uses the Python 3.9 kernel. If you need a different combination of Python and TensorFlow/PyTorch versions, you'll need to construct an appropriate custom Conda environment (e.g. miniconda3 or anaconda).
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