Overview

Compute Resources

More detailed information on system resources can be found on our Compute Resources page.

Containers with GPU Support

Singularity containers are available as modules on HPC for GPU-supported workflows. For more information, see our documentation on Containers.

Accessing GPUs

Information on how to request GPUs using SLURM can be found in our SLURM Documentation.

Training

For a list of training resources related to GPU workflows, see our Training documentation.

Cluster Information

Puma

Puma has a different arrangement for GPU nodes than Ocelote and ElGato. Whereas the older clusters have one GPU per node, Puma has four. This has a financial advantage for providing GPU's with lower overall cost, and a technical advantage of allowing jobs that can use multiple GPU's to run faster than spanning multiple nodes.  This capability comes from using a newer operating system.  
Each node has four Nvidia V100S model GPUs. They are provisioned with 32GB memory compared to 16GB on the P100's.

Ocelote

Ocelote has 45 compute nodes with Nvidia P100 GPUs that are available to researchers on campus. The limitation is a maximum of 10 concurrent jobs. One node with a V100 is also available. Since there is only one, you can feel free to use it for testing and comparisons to the P100, but production work should be run on the P100's. There is also one node with two P100's for testing jobs that use two GPU's. This one should be used to compare with running a job on two nodes.  

Cuda Modules

The latest Cuda module available on the system is 11.0 and is the only version until newer ones come along. The Cuda driver version can be queried with the nvidia-smi command. To see the modules available, in an interactive session simply run:

$ module avail cuda

OpenACC

The OpenACC API is a collection of compiler directives and runtime routines that allow you to specify loops and regions of code in standard C, C++, and Fortran that you can offload from a host CPU to the GPU.

We provide two methods of support for OpenACC

1. We support OpenACC in the PGI Compiler.  The PGI implementation of OpenACC is considered the best implementation.  
   "module load pgi" on Ocelote. If you are on a GPU node from an interactive session you can run "pgaccelinfo" to test functionality.  Remember that the login nodes do not have GPUs or software installed.  
   A useful getting-started guide written by Nvidia is available here: https://www.pgroup.com/doc/openacc17_gs.pdf 
   
   
2. We support OpenACC in the GCC Compiler 6.1 which is automatically loaded as a module when you log into Ocelote.  Verify with "module list".
   The GCC 6 release includes a much improved implementation of the OpenACC 2.0a specification.
   A useful quick reference guide is available from: https://gcc.gnu.org/wiki/OpenACC#Quick_Reference_Guide

Applications

Many applications have been optimized to run faster on GPU's. These include:

Python ML/DL including Nvidia RAPIDS 

Nvidia Rapids is only available on Ocelote currently.
Tensorflow and tensorboard are available on both Puma (python/3.8) and Ocelote (ocelote/3.6)  
The minimum version of Python that is supported is 3.6 so you will module load python/3.6 on Ocelote, for all these functions. This will get you to installed packages including: