Two BRCF research pods have NVIDIA GPU servers; however their use is restricted to the groups who own those pods.
...
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
...
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).
GROMACS
An NVIDIA GPU-enabled version of the Molecular Dynamics (MD) GROMACS program is available on all NVIDIA GPU servers, and a CPU-only version is installed also.
The /stor/scratch/GROMACS directory has several useful resources:
- benchmarks/ - a set of MD benchmark files from https://www.mpinat.mpg.de/grubmueller/bench
- gromacs_nvidia_example.sh - a simple GROMACS example script taking advantage of the GPU, running the benchMEM.tpr benchmark by default.
- gromacs_cpu_example.sh - an GROMACS example script using the CPUs only.
Resources
CUDA
Both hfogcomp04 and wilkcomp03 have both CUDA 11.8 and CUDA 12.x installed, under version-specific subdirectories of /usr/local.
...