{"id":29426,"date":"2024-09-29T17:32:08","date_gmt":"2024-09-29T14:32:08","guid":{"rendered":"https:\/\/hgpu.org\/?p=29426"},"modified":"2024-09-29T17:32:08","modified_gmt":"2024-09-29T14:32:08","slug":"openacc-offloading-of-the-mfc-compressible-multiphase-flow-solver-on-amd-and-nvidia-gpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=29426","title":{"rendered":"OpenACC offloading of the MFC compressible multiphase flow solver on AMD and NVIDIA GPUs"},"content":{"rendered":"<p>GPUs are the heart of the latest generations of supercomputers. We efficiently accelerate a compressible multiphase flow solver via OpenACC on NVIDIA and AMD Instinct GPUs. Optimization is accomplished by specifying the directive clauses &#8216;gang vector&#8217; and &#8216;collapse&#8217;. Further speedups of six and ten times are achieved by packing user-defined types into coalesced multidimensional arrays and manual inlining via metaprogramming. Additional optimizations yield seven-times speedup in array packing and thirty-times speedup of select kernels on Frontier. Weak scaling efficiencies of 97% and 95% are observed when scaling to 50% of Summit and 95% of Frontier. Strong scaling efficiencies of 84% and 81% are observed when increasing the device count by a factor of 8 and 16 on V100 and MI250X hardware. The strong scaling efficiency of AMD&#8217;s MI250X increases to 92% when increasing the device count by a factor of 16 when GPU-aware MPI is used for communication.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>GPUs are the heart of the latest generations of supercomputers. We efficiently accelerate a compressible multiphase flow solver via OpenACC on NVIDIA and AMD Instinct GPUs. Optimization is accomplished by specifying the directive clauses &#8216;gang vector&#8217; and &#8216;collapse&#8217;. Further speedups of six and ten times are achieved by packing user-defined types into coalesced multidimensional arrays [&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":[104,3],"tags":[2099,7,1600,1795,242,20,1321,176,1963],"class_list":["post-29426","post","type-post","status-publish","format-standard","hentry","category-fluid-dynamics","category-paper","tag-amd-radeon-instinct-mi250x","tag-ati","tag-cfd","tag-fluid-dynamics","tag-mpi","tag-nvidia","tag-openacc","tag-package","tag-tesla-v100"],"views":1459,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/29426","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=29426"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/29426\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=29426"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=29426"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=29426"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}