{"id":7925,"date":"2012-07-17T17:56:48","date_gmt":"2012-07-17T14:56:48","guid":{"rendered":"http:\/\/hgpu.org\/?p=7925"},"modified":"2012-07-17T17:56:48","modified_gmt":"2012-07-17T14:56:48","slug":"multicore-and-manycore-algorithms-for-octrees","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7925","title":{"rendered":"Multicore and Manycore Algorithms for Octrees"},"content":{"rendered":"<p>Octrees and compressed octrees are frequently used to represent data in an hierarchical form for high performance computing, graphics and database applications. Applications like N-body problems require building octrees multiple times. Therefore, efficient construction of octrees is critical to the efficiency of the entire applications. With ever increasing data size, there is a requirement to develop parallel algorithms for creating large octrees. We propose efficient algorithms to create octrees in parallel for manycore and multicore systems which use shared memory architecture and also give their runtime analysis. Our experimental results show that GPU octree implementation is 11.85 times faster than its sequential implementation on CPU.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Octrees and compressed octrees are frequently used to represent data in an hierarchical form for high performance computing, graphics and database applications. Applications like N-body problems require building octrees multiple times. Therefore, efficient construction of octrees is critical to the efficiency of the entire applications. With ever increasing data size, there is a requirement to [&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":[1787,1782,14,667,20,379,390],"class_list":["post-7925","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-databases","tag-nvidia","tag-nvidia-geforce-gtx-480","tag-thesis"],"views":2130,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7925","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=7925"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7925\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7925"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7925"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7925"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}