{"id":7746,"date":"2012-06-14T23:56:08","date_gmt":"2012-06-14T20:56:08","guid":{"rendered":"http:\/\/hgpu.org\/?p=7746"},"modified":"2012-06-14T23:56:08","modified_gmt":"2012-06-14T20:56:08","slug":"exploiting-unexploited-computing-resources-for-computational-logics","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7746","title":{"rendered":"Exploiting Unexploited Computing Resources for Computational Logics"},"content":{"rendered":"<p>We present an investigation of the use of GPGPU techniques to parallelize the execution of a satisfiability solver, based on the traditional DPLL procedure &#8211; which, in spite of its simplicity, still represents the core of the most competitive solvers. The investigation tackles some interesting problems, including the use of a predominantly data-parallel architecture, like NVIDIA&#8217;s CUDA platform, for the execution of relatively &quot;heavy&quot; threads, associated to traditionally sequential computations (e.g., unit propagation), non-deterministic computations (e.g., variable splitting), and meta-heuristics to guide search. Experimentation confirms the potential for significant speedups from the use of GPGPUs, even with relatively simple modifications to the structure of the DPLL procedures &#8211; which should facilitate the porting of such ideas to other DPLL-based solvers.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present an investigation of the use of GPGPU techniques to parallelize the execution of a satisfiability solver, based on the traditional DPLL procedure &#8211; which, in spite of its simplicity, still represents the core of the most competitive solvers. The investigation tackles some interesting problems, including the use of a predominantly data-parallel architecture, like [&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":[11,89,3],"tags":[1782,14,20,1090,751,1226],"class_list":["post-7746","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-560","tag-sat","tag-tesla-c2075"],"views":2238,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7746","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=7746"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7746\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7746"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7746"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7746"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}