{"id":4173,"date":"2011-05-29T20:41:01","date_gmt":"2011-05-29T20:41:01","guid":{"rendered":"http:\/\/hgpu.org\/?p=4173"},"modified":"2011-05-29T20:41:01","modified_gmt":"2011-05-29T20:41:01","slug":"efficent-multiple-pass-multiple-output-algorithms-on-the-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=4173","title":{"rendered":"Efficent multiple pass, multiple output algorithms on the GPU"},"content":{"rendered":"<p>This paper presents an evaluation of the state of the art in Graphics Processing Unit (GPU) algorithmic techniques. Fast, portable methods for maintaining state between independent passes of an algorithm are compared and contrasted with similar techniques from the previous generation, which are still in widespread use in the graphical and non-graphical fields today. The performance of these features in isolation and in unison with real algorithms is measured and contrasted with that of existing techniques. A revised set of constraints on the GPU programming model is presented in light of these findings.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents an evaluation of the state of the art in Graphics Processing Unit (GPU) algorithmic techniques. Fast, portable methods for maintaining state between independent passes of an algorithm are compared and contrasted with similar techniques from the previous generation, which are still in widespread use in the graphical and non-graphical fields today. The [&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":[1787,1782,20,301,182,67,70],"class_list":["post-4173","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-gt","tag-opengl","tag-performance","tag-programming-techniques"],"views":2182,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4173","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=4173"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4173\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4173"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4173"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4173"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}