Software Packages
Conventional Operations
For using conventional tools such as conda, VSCode, pytorch, tensorflow, or containers with the Galvani cluster, please read through the Tutorials in this User Guide for instructions.
Compute-node deployment
Software deployed on a sample public compute node:
| Node | CPU model | CUDA paths | Interconnect | /scratch_local |
|---|---|---|---|---|
galvani-cn053 |
Intel(R) Xeon(R) Gold 5218 CPU @ 2.30GHz | /usr/local/cuda, /usr/local/cuda-11, /usr/local/cuda-11.8, /usr/local/cuda-12, /usr/local/cuda-12.1, /usr/local/cuda-12.8, /usr/local/cuda-13, /usr/local/cuda-13.0 |
40 GbE | 320G xfs |
galvani-cn057 |
Intel(R) Xeon(R) Gold 5220 CPU @ 2.20GHz | /usr/local/cuda, /usr/local/cuda-11, /usr/local/cuda-11.8, /usr/local/cuda-12, /usr/local/cuda-12.1, /usr/local/cuda-12.8, /usr/local/cuda-13, /usr/local/cuda-13.0 |
40 GbE (ConnectX-4 Lx) | 3.0T xfs |
galvani-cn059 |
Intel(R) Xeon(R) Gold 5218 CPU @ 2.30GHz | /usr/local/cuda, /usr/local/cuda-11, /usr/local/cuda-11.8, /usr/local/cuda-12, /usr/local/cuda-12.1, /usr/local/cuda-12.8, /usr/local/cuda-13, /usr/local/cuda-13.0 |
40 GbE | 1.2T xfs |
galvani-cn102 |
Intel(R) Xeon(R) Gold 6240 CPU @ 2.60GHz | /usr/local/cuda, /usr/local/cuda-11, /usr/local/cuda-11.8, /usr/local/cuda-12, /usr/local/cuda-12.1, /usr/local/cuda-12.8, /usr/local/cuda-13, /usr/local/cuda-13.0 |
40 GbE | — |
galvani-cn201 |
AMD EPYC 7302 16-Core Processor | /usr/local/cuda, /usr/local/cuda-11, /usr/local/cuda-11.8, /usr/local/cuda-12, /usr/local/cuda-12.1, /usr/local/cuda-12.8, /usr/local/cuda-13, /usr/local/cuda-13.0 |
40 GbE | 3.5T xfs |
galvani-cn227 |
AMD EPYC 7742 64-Core Processor | /usr/local/cuda, /usr/local/cuda-11, /usr/local/cuda-11.8, /usr/local/cuda-12, /usr/local/cuda-12.1, /usr/local/cuda-12.8, /usr/local/cuda-13, /usr/local/cuda-13.0 |
40 GbE | 25T xfs |
galvani-cn240 |
AMD EPYC 7302 16-Core Processor | /usr/local/cuda, /usr/local/cuda-11, /usr/local/cuda-11.8, /usr/local/cuda-12, /usr/local/cuda-12.1, /usr/local/cuda-12.8, /usr/local/cuda-13, /usr/local/cuda-13.0 |
40 GbE | 1.5T xfs |
Development Tools
To see the development tools installed on Galvani run the following:
scl list-collections
The currently available toolsets are:
| Toolset |
|---|
gcc-toolset-10 |
gcc-toolset-11 |
gcc-toolset-9 |
To source and enable one of these, run:
source scl_source enable <devtoolset-x>
Selecting a CUDA version
The CUDA versions installed on compute nodes are listed in the "Compute-node deployment" table above.
You can select a specific CUDA version to use (e.g. cuda 11.8) by running (replace 11.8 with the desired CUDA version):
export CUDA_VER=11.8 ; export PATH=/usr/local/cuda-$CUDA_VER/bin:$PATH ; export LD_LIBRARY_PATH=/usr/local/cuda-$CUDA_VER/lib64:/usr/local/cuda-$CUDA_VER/extras/CUPTI/lib64:$LD_LIBRARY_PATH
PATH and LD_LIBRARY_PATH for this session.
You can verify this worked by running nvcc --version and reading the output.
If you want to select the same CUDA version every time, please add the above command to your .bashrc file or sbatch file.
Last update:
July 17, 2026
Created: June 21, 2024
Created: June 21, 2024