{"id":10532,"date":"2013-09-15T22:18:54","date_gmt":"2013-09-15T19:18:54","guid":{"rendered":"http:\/\/hgpu.org\/?p=10532"},"modified":"2013-09-15T22:18:54","modified_gmt":"2013-09-15T19:18:54","slug":"quine-mccluskey-algorithm-on-gpgpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=10532","title":{"rendered":"Quine-McCluskey algorithm on GPGPU"},"content":{"rendered":"<p>This paper deals with parallelization of the Quine-McCluskey algorithm. This boolean function minimization algorithm has a limitation when dealing with more than four variables. The problem computed by this algorithm is NP-hard and runtime of the algorithm grows exponentially with the number of variables. The goal is to show that parallel implementation of the Quine-McCluskey algorithm on graphics processing units (GPUs) brings significant acceleration of computing process. Parallelization of the algorithm is implemented through Compute Unified Device Architecture (CUDA), which is a parallel computing platform and programming model created by NVIDIA and implemented by graphics processing units (GPUs).<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper deals with parallelization of the Quine-McCluskey algorithm. This boolean function minimization algorithm has a limitation when dealing with more than four variables. The problem computed by this algorithm is NP-hard and runtime of the algorithm grows exponentially with the number of variables. The goal is to show that parallel implementation of the Quine-McCluskey [&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":[1787,1782,14,20,554,1497],"class_list":["post-10532","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-nvidia","tag-nvidia-geforce-9800-gt","tag-nvidia-geforce-gt-550-ti"],"views":3139,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10532","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=10532"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10532\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10532"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10532"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10532"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}