{"id":8628,"date":"2012-12-10T23:09:30","date_gmt":"2012-12-10T21:09:30","guid":{"rendered":"http:\/\/hgpu.org\/?p=8628"},"modified":"2012-12-10T23:09:30","modified_gmt":"2012-12-10T21:09:30","slug":"a-gpu-based-parallel-algorithm-for-design-structure-matrix-dsm-partition","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8628","title":{"rendered":"A GPU-Based Parallel Algorithm for Design Structure Matrix (DSM) Partition"},"content":{"rendered":"<p>In complicated system manufacturing and designing, the DSM has been proved to be powerful and effective for analyzing and optimizing the executional order of tasks. Many algorithms have been proposed to optimize the DSM, however, with the system complexity increasing, the number of tasks involved enlarges, which results in the rapid growth of time cost in the algorithms. In this paper, we propose a parallel algorithm using GPU to calculate the DSM Partition, and compare with other algorithm which shows the time cost greatly reduced.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In complicated system manufacturing and designing, the DSM has been proved to be powerful and effective for analyzing and optimizing the executional order of tasks. Many algorithms have been proposed to optimize the DSM, however, with the system complexity increasing, the number of tasks involved enlarges, which results in the rapid growth of time cost [&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":[1787,1782,14,20,609],"class_list":["post-8628","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-software-engineering"],"views":2597,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8628","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=8628"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8628\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8628"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8628"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8628"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}