{"id":2678,"date":"2011-02-01T15:51:44","date_gmt":"2011-02-01T15:51:44","guid":{"rendered":"http:\/\/hgpu.org\/?p=2678"},"modified":"2011-02-01T15:51:44","modified_gmt":"2011-02-01T15:51:44","slug":"gpu-as-a-parallel-machine-sorting-on-the-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2678","title":{"rendered":"GPU as a Parallel Machine: Sorting on the GPU"},"content":{"rendered":"<p>Sorting is a fundamental algorithmic building block. One of the most studied problems in computer science is ordering a list of items efficiently. Buck and Purcell showed how the parallel bitonic merge sort algorithm, could exploit many of the parallel features of the SIMD architecture of the GPU. Efficient sorting has practical importance to optimizing algorithms that require sorted lists to work correctly. Moreover, the cost of reading data back from the GPU to the CPU is incredibly inefficient; sorting the data directly on the GPU is preferable for many graphics algorithms.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Sorting is a fundamental algorithmic building block. One of the most studied problems in computer science is ordering a list of items efficiently. Buck and Purcell showed how the parallel bitonic merge sort algorithm, could exploit many of the parallel features of the SIMD architecture of the GPU. Efficient sorting has practical importance to optimizing [&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":[36,11,3],"tags":[1787,1782,20,385,9],"class_list":["post-2678","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-paper","tag-algorithms","tag-computer-science","tag-nvidia","tag-nvidia-geforce-6800-ultra","tag-sorting"],"views":2091,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2678","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=2678"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2678\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2678"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2678"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2678"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}