{"id":4747,"date":"2011-07-12T21:31:54","date_gmt":"2011-07-12T18:31:54","guid":{"rendered":"http:\/\/hgpu.org\/?p=4747"},"modified":"2011-07-12T21:31:54","modified_gmt":"2011-07-12T18:31:54","slug":"accelerating-the-nussinov-rna-folding-algorithm-with-cudagpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=4747","title":{"rendered":"Accelerating the Nussinov RNA folding algorithm with CUDA\/GPU"},"content":{"rendered":"<p>Graphics processing units (GPU) on commodity video cards have evolved into powerful computational devices. The RNA secondary structure arises from the primary structure and a backbone of canonical, Watson-Crick base pairings (A-U, C-G), and to a lesser extent, the G-U pairing. Early computational work by Nussinov formulated the problem of RNA secondary structure prediction as a maximization of the number of paired bases, which led to a simplified problem amenable to a dynamic programming solution for O(n^3) serial time. This article describes a GPU implementation of the Nussinov dynamic programming. Computation results show that the GPU implementation is up to 290 times faster than the CPU.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Graphics processing units (GPU) on commodity video cards have evolved into powerful computational devices. The RNA secondary structure arises from the primary structure and a backbone of canonical, Watson-Crick base pairings (A-U, C-G), and to a lesser extent, the G-U pairing. Early computational work by Nussinov formulated the problem of RNA secondary structure prediction as [&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":[10,89,3],"tags":[1781,14,387,20,199,378,202],"class_list":["post-4747","post","type-post","status-publish","format-standard","hentry","category-biology","category-nvidia-cuda","category-paper","tag-biology","tag-cuda","tag-macromolecule","tag-nvidia","tag-tesla-c1060","tag-tesla-c2050","tag-tesla-c870"],"views":2784,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4747","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=4747"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4747\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4747"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4747"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4747"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}