{"id":2892,"date":"2011-02-18T17:47:42","date_gmt":"2011-02-18T17:47:42","guid":{"rendered":"http:\/\/hgpu.org\/?p=2892"},"modified":"2011-02-18T17:47:42","modified_gmt":"2011-02-18T17:47:42","slug":"gpu-acceleration-of-near-minimal-logic-minimization","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2892","title":{"rendered":"GPU Acceleration of Near-Minimal Logic Minimization"},"content":{"rendered":"<p>In this paper, we describe a GPU-accelerated implementation of a logic minimization heuristic based on the near minimal approach. This algorithm has three key kernel computations, and the current version of our implementation, we adapted one of these kernels for GPU execution. In this paper we report our results gained from using NVIDIA&#8217;s CUDA development framework and an NVIDIA Tesla GPUs, achieving a nearly 10X speedup as compared to a software implementation executed on a Xeon 5500-series processor.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we describe a GPU-accelerated implementation of a logic minimization heuristic based on the near minimal approach. This algorithm has three key kernel computations, and the current version of our implementation, we adapted one of these kernels for GPU execution. In this paper we report our results gained from using NVIDIA&#8217;s CUDA development [&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":[1782,14,452,20,244],"class_list":["post-2892","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-heterogeneous-systems","tag-nvidia","tag-tesla-s1070"],"views":1762,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2892","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=2892"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2892\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2892"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2892"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2892"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}