Abhinav Thota

The CI-PIVOT project, a collaboration between Indiana University, Purdue, and Mississippi State, addresses the gap between complex HPC/AI resources and domain researchers by transforming science experts into embedded Cyberinfrastructure Professionals (CIPs). Through an 18-month cohort model, this project establishes a scalable framework for professionalizing the CIP role, providing insights into training that can be generalized across the broader research ecosystem.

For systems with an accelerator component, it is important from an operational and planning perspective to understand how and to what extent the accelerators are being used. Having a framework for tracking the utilization of accelerator resources is important both for judging how efficiently used a system is and for capacity and configuration planning of future systems. In addition to tracking total utilization and accelerator efficiency numbers, some attention should also be paid to the types of research and workflows that are being executed on the system. In the past, the demand for accelerator resources was largely driven by more traditional simulation codes, such as molecular dynamics. But with the growing popularity of deep learning and artificial intelligence workflows, accelerators have become even more highly sought after and are being used in new ways. Provisioning resources to researchers via an allocation system allows sites to track a project's usage and workflow as well as the scientific impact of the project. With such tools and data in hand, characterizing the GPU utilization of deep learning frameworks versus more traditional GPU‐enabled applications becomes possible. In this paper we present a survey of GPU monitoring tools used in sites and a framework for tracking the utilization of NVIDIA GPUs on Slurm‐scheduled HPC systems used at Indiana University. We also present an analysis of accelerator utilization on multiple systems, including an HPE Apollo system targeting AI workflows and a Cray EX system.

High Performance Research Desktops are used by HPC centers and research computing organizations to lower the barrier of entry to HPC systems. These Linux desktops are deployed alongside HPC systems, leveraging the investments in HPC compute and storage infrastructure. By serving as a gateway to HPC systems they provide users with an environment to perform setup and infrastructure tasks related to the actual HPC work. Such tasks can take significant amounts of time, are vital to the successful use of HPC systems, and can benefit from a graphical desktop environment. In addition to serving as a gateway to HPC systems, High Performance Research Desktops are also used to run interactive graphical applications like MATLAB, RStudio or VMD. This paper defines the concept of High Performance Research Desktops and summarizes use cases from Indiana University, Lund University and Technical University of Denmark, which have implemented and operated such a system for more than 10 years. Based on these use cases, possible future directions are presented.