{"id":11173,"date":"2014-01-02T23:50:54","date_gmt":"2014-01-02T21:50:54","guid":{"rendered":"http:\/\/hgpu.org\/?p=11173"},"modified":"2014-01-02T23:50:54","modified_gmt":"2014-01-02T21:50:54","slug":"achieving-teracups-on-longest-common-subsequence-problem-using-gpgpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=11173","title":{"rendered":"Achieving TeraCUPS on Longest Common Subsequence Problem using GPGPUs"},"content":{"rendered":"<p>In this paper, we describe a novel technique to optimize longest common subsequence (LCS) algorithm for one-to-many matching problem on GPUs by transforming the computation into bit-wise operations and a post-processing step. The former can be highly optimized and achieves more than a trillion operations (cell updates) per second (CUPS)-a first for LCS algorithms. The latter is more efficiently done on CPUs, in a fraction of the bit-wise computation time. The bit-wise step promises to be a foundational step and a fundamentally new approach to developing algorithms for increasingly popular heterogeneous environments that could dramatically increase the applicability of hybrid CPU-GPU environments.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we describe a novel technique to optimize longest common subsequence (LCS) algorithm for one-to-many matching problem on GPUs by transforming the computation into bit-wise operations and a post-processing step. The former can be highly optimized and achieves more than a trillion operations (cell updates) per second (CUPS)-a first for LCS algorithms. The [&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":[1787,1782,14,452,20,379,1389,209,717,1226,1241],"class_list":["post-11173","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-heterogeneous-systems","tag-nvidia","tag-nvidia-geforce-gtx-480","tag-nvidia-geforce-gtx-690","tag-sequence-alignment","tag-sequence-matching","tag-tesla-c2075","tag-tesla-m2090"],"views":3238,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11173","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=11173"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11173\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11173"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11173"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11173"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}