{"id":28879,"date":"2023-12-18T11:10:18","date_gmt":"2023-12-18T09:10:18","guid":{"rendered":"https:\/\/hgpu.org\/?p=28879"},"modified":"2023-12-18T11:10:18","modified_gmt":"2023-12-18T09:10:18","slug":"principles-for-automated-and-reproducible-benchmarking","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=28879","title":{"rendered":"Principles for Automated and Reproducible Benchmarking"},"content":{"rendered":"<p>The diversity in processor technology used by High Performance Computing (HPC) facilities is growing, and so applications must be written in such a way that they can attain high levels of performance across a range of different CPUs, GPUs, and other accelerators. Measuring application performance across this wide range of platforms becomes crucial, but there are significant challenges to do this rigorously, in a time efficient way, whilst assuring results are scientifically meaningful, reproducible, and actionable. This paper presents a methodology for measuring and analysing the performance portability of a parallel application and shares a software framework which combines and extends adopted technologies to provide a usable benchmarking tool. We demonstrate the flexibility and effectiveness of the methodology and benchmarking framework by showcasing a variety of benchmarking case studies which utilise a stable of supercomputing resources at a national scale.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The diversity in processor technology used by High Performance Computing (HPC) facilities is growing, and so applications must be written in such a way that they can attain high levels of performance across a range of different CPUs, GPUs, and other accelerators. Measuring application performance across this wide range of platforms becomes crucial, but there [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,3],"tags":[451,1782,1682,20,2118,176,1586],"class_list":["post-28879","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-benchmarking","tag-computer-science","tag-hpc","tag-nvidia","tag-oneapi","tag-package","tag-performance-portability"],"views":1514,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/28879","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=28879"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/28879\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=28879"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=28879"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=28879"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}