{"id":9838,"date":"2013-07-08T23:16:46","date_gmt":"2013-07-08T20:16:46","guid":{"rendered":"http:\/\/hgpu.org\/?p=9838"},"modified":"2013-07-08T23:16:46","modified_gmt":"2013-07-08T20:16:46","slug":"coalition-structure-generation-with-the-graphic-processor-unit","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=9838","title":{"rendered":"Coalition Structure Generation with the Graphic Processor Unit"},"content":{"rendered":"<p>Coalition Structure Generation-the problem of finding the optimal set of coalitions &#8211; has received considerable attention in recent AI literature. The fastest exact algorithm to solve this problem is IDP-IP*, due to Rahwan et al. (2012). This algorithm is a hybrid of two previous algorithms, namely IDP and IP. As such, it is desirable to speed up IDP as this will, in turn, improve upon the state-of-the-art. In this paper, we present IDPG-the first coalition structure generation algorithm based on the Graphics Processing Unit (GPU). This follows a promising, new algorithm design paradigm that can provide significant speed ups. We show that IDPG is faster than IDP by two orders of magnitude.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Coalition Structure Generation-the problem of finding the optimal set of coalitions &#8211; has received considerable attention in recent AI literature. The fastest exact algorithm to solve this problem is IDP-IP*, due to Rahwan et al. (2012). This algorithm is a hybrid of two previous algorithms, namely IDP and IP. As such, it is desirable to [&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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,11,89,3],"tags":[1787,117,1782,14,20,1436],"class_list":["post-9838","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-artificial-intelligence","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-660"],"views":2176,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9838","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=9838"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9838\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9838"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9838"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9838"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}