{"id":6868,"date":"2012-01-08T16:56:37","date_gmt":"2012-01-08T14:56:37","guid":{"rendered":"http:\/\/hgpu.org\/?p=6868"},"modified":"2012-01-08T16:56:37","modified_gmt":"2012-01-08T14:56:37","slug":"parameter-tuning-of-a-hybrid-treecode-fmm-on-gpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6868","title":{"rendered":"Parameter Tuning of a Hybrid Treecode-FMM on GPUs"},"content":{"rendered":"<p>Treecodes are O(N log N) hierarchical N-body algorithms, which have traditionally been used for applications in astrophysics, in a low-accuracy regime. Fast multipole methods (FMM) are O(N) hierarchical N-body algorithms that have been used in a variety of applications, often in the high-accuracy regime. Both algorithms are known to perform well on massively parallel heterogeneous systems, with the treecode showing stronger speed-ups due to higher computational intensity. We propose a hybridization of the two algorithms and aim to determine whether such approach can best either algorithm on its own. We study the behavior of the hybrid treecode-FMM for different accuracies, along with its performance on GPUs.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Treecodes are O(N log N) hierarchical N-body algorithms, which have traditionally been used for applications in astrophysics, in a low-accuracy regime. Fast multipole methods (FMM) are O(N) hierarchical N-body algorithms that have been used in a variety of applications, often in the high-accuracy regime. Both algorithms are known to perform well on massively parallel heterogeneous [&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":[36,96,89,3,12],"tags":[1787,1794,14,723,452,258,20,1783,931],"class_list":["post-6868","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-astrophysics","category-nvidia-cuda","category-paper","category-physics","tag-algorithms","tag-astrophysics","tag-cuda","tag-fast-multipole-method","tag-heterogeneous-systems","tag-n-body-simulation","tag-nvidia","tag-physics","tag-tesla-m2050"],"views":2083,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6868","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=6868"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6868\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6868"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6868"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6868"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}