{"id":7727,"date":"2012-06-09T15:53:32","date_gmt":"2012-06-09T12:53:32","guid":{"rendered":"http:\/\/hgpu.org\/?p=7727"},"modified":"2012-06-09T15:53:32","modified_gmt":"2012-06-09T12:53:32","slug":"scaling-fast-multipole-methods-up-to-4000-gpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7727","title":{"rendered":"Scaling Fast Multipole Methods up to 4000 GPUs"},"content":{"rendered":"<p>The Fast Multipole Method (FMM) is a hierarchical N-body algorithm with linear complexity, high arithmetic intensity, high data locality, has hierarchical communication patterns, and no global synchronization. The combination of these features allows the FMM to scale well on large GPU based systems, and to use their compute capability effectively.  We present a 1 PFlop\/s calculation of isotropic turbulence with 64 billion vortex particles using 4096 GPUs on the TSUBAME 2.0 system.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Fast Multipole Method (FMM) is a hierarchical N-body algorithm with linear complexity, high arithmetic intensity, high data locality, has hierarchical communication patterns, and no global synchronization. The combination of these features allows the FMM to scale well on large GPU based systems, and to use their compute capability effectively. We present a 1 PFlop\/s [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,11,89,3],"tags":[1787,1782,14,723,242,258,20,931],"class_list":["post-7727","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-fast-multipole-method","tag-mpi","tag-n-body-simulation","tag-nvidia","tag-tesla-m2050"],"views":3078,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7727","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=7727"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7727\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7727"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7727"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7727"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}