{"id":8345,"date":"2012-10-13T04:46:46","date_gmt":"2012-10-13T01:46:46","guid":{"rendered":"http:\/\/hgpu.org\/?p=8345"},"modified":"2012-10-13T04:46:46","modified_gmt":"2012-10-13T01:46:46","slug":"programming-nvidia-cards-by-means-of-transitive-closure-based-parallelization-algorithms","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8345","title":{"rendered":"Programming NVIDIA cards by means of transitive closure based parallelization algorithms"},"content":{"rendered":"<p>Massively parallel processing is a type of computing that uses many separate CPUs or GPUs running in parallel to execute a single program. Because most computations are contained in program loops, automatic extraction of parallelism available in loops is extremely important for many-core systems. In this paper, we study speed-up and scalability of parallel code scanning synchronization-free slices and time partitions by means of a 960 CUDA Cores machine, Tesla S1070.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Massively parallel processing is a type of computing that uses many separate CPUs or GPUs running in parallel to execute a single program. Because most computations are contained in program loops, automatic extraction of parallelism available in loops is extremely important for many-core systems. In this paper, we study speed-up and scalability of parallel code [&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":[11,89,3],"tags":[1782,14,20,67,244],"class_list":["post-8345","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-nvidia","tag-performance","tag-tesla-s1070"],"views":1794,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8345","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=8345"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8345\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8345"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8345"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8345"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}