{"ID":2855561,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.12166","arxiv_id":"2510.12166","title":"Comparing Cross-Platform Performance via Node-to-Node Scaling Studies","abstract":"Due to the increasing diversity of high-performance computing architectures, researchers and practitioners are increasingly interested in comparing a code's performance and scalability across different platforms. However, there is a lack of available guidance on how to actually set up and analyze such cross-platform studies. In this paper, we contend that the natural base unit of computing for such studies is a single compute node on each platform and offer guidance in setting up, running, and analyzing node-to-node scaling studies. We propose templates for presenting scaling results of these studies and provide several case studies highlighting the benefits of this approach.","short_abstract":"Due to the increasing diversity of high-performance computing architectures, researchers and practitioners are increasingly interested in comparing a code's performance and scalability across different platforms. However, there is a lack of available guidance on how to actually set up and analyze such cross-platform st...","url_abs":"https://arxiv.org/abs/2510.12166","url_pdf":"https://arxiv.org/pdf/2510.12166v1","authors":"[\"Kenneth Weiss\",\"Thomas M. Stitt\",\"Daryl Hawkins\",\"Olga Pearce\",\"Stephanie Brink\",\"Robert N. Rieben\"]","published":"2025-10-14T05:47:25Z","proceeding":"cs.DC","tasks":"[\"cs.DC\"]","methods":"[]","has_code":false}