Workload characterization is an integral part of performance analysis of high performance computing (HPC) systems. An understanding of workload properties sheds light on resource utilization and can be used to inform performance optimization both at the software and system configuration levels. It can provide information on how computational science usage modalities are changing that could potentially aid holistic capacity planning for the wider HPC ecosystem. Here, we report on the results of a detailed workload analysis of the portfolio of supercomputers comprising the NSF Innovative HPC program in order to characterize its past and current workload and look for trends to understand the nature of how the broad portfolio of computational science research is being supported and how it is changing over time. The workload analysis also sought to illustrate a wide variety of usage patterns and performance requirements for jobs running on these systems. File system performance, memory utilization and the types of parallelism employed by users (MPI, threads, etc) were also studied for all systems for which job level performance data was available.
@article{arxiv.1801.04306,
title = {A Workload Analysis of NSF's Innovative HPC Resources Using XDMoD},
author = {Nikolay A. Simakov and Joseph P. White and Robert L. DeLeon and Steven M. Gallo and Matthew D. Jones and Jeffrey T. Palmer and Benjamin Plessinger and Thomas R. Furlani},
journal= {arXiv preprint arXiv:1801.04306},
year = {2018}
}