{"id":3478,"date":"2011-04-06T20:20:09","date_gmt":"2011-04-06T20:20:09","guid":{"rendered":"http:\/\/hgpu.org\/?p=3478"},"modified":"2011-04-06T20:20:09","modified_gmt":"2011-04-06T20:20:09","slug":"barnes-hut-treecode-on-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3478","title":{"rendered":"Barnes-hut treecode on GPU"},"content":{"rendered":"<p>General-purpose computation on graphics processing units (GPGPU) has become a popular field of study. Due to its high computing capacity and relatively low price, GPU has been an ideal processing unit for many scientific applications, among which is N-body simulation. According to the published papers, a simple O(N^2) algorithm of N-body simulation has achieved some enhancements, but tree-algorithm doesn&#8217;t work well on GPU. This paper proposes a new implementation of tree-algorithm on GPU using CUDA, which has obtained more than 100X speedup when computing forces between bodies. This paper also rises up a new method to build tree in this algorithm, making the performance even better.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>General-purpose computation on graphics processing units (GPGPU) has become a popular field of study. Due to its high computing capacity and relatively low price, GPU has been an ideal processing unit for many scientific applications, among which is N-body simulation. According to the published papers, a simple O(N^2) algorithm of N-body simulation has achieved some [&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":[1787,1782,14,258,20],"class_list":["post-3478","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-n-body-simulation","tag-nvidia"],"views":2312,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3478","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=3478"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3478\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3478"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3478"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3478"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}