{"id":8348,"date":"2012-10-13T04:48:03","date_gmt":"2012-10-13T01:48:03","guid":{"rendered":"http:\/\/hgpu.org\/?p=8348"},"modified":"2012-10-13T04:48:03","modified_gmt":"2012-10-13T01:48:03","slug":"fast-parallel-implementation-of-fractional-packing-and-covering-linear-programs","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8348","title":{"rendered":"Fast Parallel Implementation of Fractional Packing and Covering Linear Programs"},"content":{"rendered":"<p>We present a parallel implementation of the randomized (1 + e)-approximation algorithm for packing and covering linear programs presented by Koufogiannakis and Young [4]. In order to make the algorithm more parallelizable we also implemented a deterministic version of the algorithm, i.e. instead of updating a single random entry at each iteration we updated deterministically many entries at once. This slowed down a single iteration of the algorithm but allowed for larger step sizes which lead to fewer iterations. We use NVIDIA&#8217;s parallel computing architecture CUDA for the parallel environment. We report a speedup over the times reported by Koufogiannakis and Young [4] between one and two orders of magnitude.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present a parallel implementation of the randomized (1 + e)-approximation algorithm for packing and covering linear programs presented by Koufogiannakis and Young [4]. In order to make the algorithm more parallelizable we also implemented a deterministic version of the algorithm, i.e. instead of updating a single random entry at each iteration we updated deterministically [&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":[36,89,157,3],"tags":[1787,14,1796,20,1006],"class_list":["post-8348","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-mathematics","category-paper","tag-algorithms","tag-cuda","tag-mathematics","tag-nvidia","tag-tesla-c2070"],"views":2340,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8348","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=8348"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8348\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8348"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8348"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8348"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}