{"id":11970,"date":"2014-05-03T02:02:59","date_gmt":"2014-05-02T23:02:59","guid":{"rendered":"http:\/\/hgpu.org\/?p=11970"},"modified":"2014-05-03T02:02:59","modified_gmt":"2014-05-02T23:02:59","slug":"coalition-structure-generation-with-the-graphics-processing-unit","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=11970","title":{"rendered":"Coalition Structure Generation with the Graphics Processing Unit"},"content":{"rendered":"<p>Coalition Structure Generation-the problem of finding the optimal division of agents into coalitions-has received considerable attention in recent AI literature. The fastest exact algorithm to solve this problem is IDP-IP* [17], which is a hybrid of two previous algorithms, namely IDP and IP. Given this, 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 speedups.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 division of agents into coalitions-has received considerable attention in recent AI literature. The fastest exact algorithm to solve this problem is IDP-IP* [17], which is a hybrid of two previous algorithms, namely IDP and IP. Given this, it is desirable to speed up IDP as this will, [&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":[11,89,3],"tags":[1782,14,20,1436,176,67],"class_list":["post-11970","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-660","tag-package","tag-performance"],"views":2455,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11970","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=11970"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11970\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11970"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11970"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11970"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}