{"id":12244,"date":"2014-06-11T00:06:56","date_gmt":"2014-06-10T21:06:56","guid":{"rendered":"http:\/\/hgpu.org\/?p=12244"},"modified":"2014-06-11T00:06:56","modified_gmt":"2014-06-10T21:06:56","slug":"parallel-prefix-scan-with-compute-unified-device-architecture-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=12244","title":{"rendered":"Parallel Prefix Scan with Compute Unified Device Architecture (CUDA)"},"content":{"rendered":"<p>Parallel prefix scan, also known as parallel prefix sum, is a building block for many parallel algorithms including polynomial evaluation, sorting and building data structures. This paper introduces prefix scan and also describes a step-by-step procedure to implement prefix scan efficiently with Compute Unified Device Architecture (CUDA). This paper starts with a basic naive algorithm and proceeds through more advanced techniques to obtain best performance.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Parallel prefix scan, also known as parallel prefix sum, is a building block for many parallel algorithms including polynomial evaluation, sorting and building data structures. This paper introduces prefix scan and also describes a step-by-step procedure to implement prefix scan efficiently with Compute Unified Device Architecture (CUDA). This paper starts with a basic naive algorithm [&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":[36,11,89,3],"tags":[1787,1782,14,20,9],"class_list":["post-12244","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-nvidia","tag-sorting"],"views":2226,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12244","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=12244"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12244\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12244"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12244"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12244"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}