The docs: https://arcs-njit-edu.github.io/Docs/Policies/wulver_policies/#faculty-computing-allowance

state:
The SUs can be renewed annually by providing a brief report describing how the SUs were used and list of publications, presentations, and awards generated from research conducted.

In the past I used DARTER at oak ridge, this was the language required in publications:

This material is based upon work performed using computational resources supported by the University of Tennessee and Oak Ridge National Laboratory’s Joint Institute for Computational Sciences (http://www.jics.utk.edu/). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the University of Tennessee, Oak Ridge National Laboratory, or the Joint Institute for Computational Sciences.

Providing text would be helpful for such expectations.

The example SLURM script here: https://arcs-njit-edu.github.io/Docs/Software/programming/python/conda/#install-tensorflow-with-gpu says --partition=datasci, but the docs don't list this as available partition https://arcs-njit-edu.github.io/Docs/Software/slurm/slurm/#using-slurm-on-cluster

The explanation for job priorities and how it is counted against SU's could be more explicit.
https://arcs-njit-edu.github.io/Docs/Policies/wulver_policies/#job-priorities

• Does it refer specifically to: #SBATCH --qos=low

• How can a user query their current usage/allocation available?

It's understandable the infrastructure is in beta, re: More details on calculating SUs for GPUs, high memory nodes, etc… will be provided at a later date.

• Does the GPU SU rule for low priority equal the CPU?