{"id":12540,"date":"2014-07-23T00:53:18","date_gmt":"2014-07-22T21:53:18","guid":{"rendered":"http:\/\/hgpu.org\/?p=12540"},"modified":"2014-07-23T00:53:18","modified_gmt":"2014-07-22T21:53:18","slug":"solution-level-parallelization-of-local-search-metaheuristic-algorithm-on-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=12540","title":{"rendered":"Solution Level Parallelization of Local Search Metaheuristic Algorithm on GPU"},"content":{"rendered":"<p>Local search metaheuristic algorithms are proven &amp; powerful combinatorial optimization strategies to tackle hard problems like traveling salesman problem. These algorithms explore &amp; evaluate neighbors of a single solution. Time Consuming LSM algorithms can be improved by parallelizing exploration &amp; evaluation of neighbors of a solution. GPU architecture is suitable for algorithms of single program multiple data parallelism. Implemented algorithm reduces time consuming memory transfers and improves computational time by efficient use of memory hierarchy.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Local search metaheuristic algorithms are proven &amp; powerful combinatorial optimization strategies to tackle hard problems like traveling salesman problem. These algorithms explore &amp; evaluate neighbors of a single solution. Time Consuming LSM algorithms can be improved by parallelizing exploration &amp; evaluation of neighbors of a solution. GPU architecture is suitable for algorithms of single program [&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":[1787,1782,14,263,748,20,1306,441],"class_list":["post-12540","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-data-parallelism","tag-metaheuristics","tag-nvidia","tag-nvidia-geforce-gtx-680","tag-search"],"views":2256,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12540","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=12540"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12540\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12540"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12540"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12540"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}