{"id":1700,"date":"2010-11-27T16:26:34","date_gmt":"2010-11-27T16:26:34","guid":{"rendered":"http:\/\/hgpu.org\/?p=1700"},"modified":"2010-11-27T16:26:34","modified_gmt":"2010-11-27T16:26:34","slug":"parallel-reconstruction-of-neighbor-joining-trees-for-large-multiple-sequence-alignments-using-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1700","title":{"rendered":"Parallel reconstruction of neighbor-joining trees for large multiple sequence alignments using CUDA"},"content":{"rendered":"<p>Computing large multiple protein sequence alignments using progressive alignment tools such as ClustalW requires several hours on state-of-the-art workstations. ClustalW uses a three-stage processing pipeline: (i) pairwise distance computation; (ii) phylogenetic tree reconstruction; and (iii) progressive multiple alignment computation. Previous work on accelerating ClustalW was mainly focused on parallelizing the first stage and achieved good speedups for a few hundred input sequences. However, if the input size grows to several thousand sequences, the second stage can dominate the overall runtime. In this paper, we present a new approach to accelerating this second stage using graphics processing units (GPUs). In order to derive an efficient mapping onto the GPU architecture, we present a parallelization of the neighbor-joining tree reconstruction algorithm using CUDA. Our experimental results show speedups of over 26\u00d7 for large datasets compared to the sequential implementation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Computing large multiple protein sequence alignments using progressive alignment tools such as ClustalW requires several hours on state-of-the-art workstations. ClustalW uses a three-stage processing pipeline: (i) pairwise distance computation; (ii) phylogenetic tree reconstruction; and (iii) progressive multiple alignment computation. Previous work on accelerating ClustalW was mainly focused on parallelizing the first stage and achieved good [&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":[123,1781,14,20,234,209,284],"class_list":["post-1700","post","type-post","status-publish","format-standard","hentry","category-biology","category-nvidia-cuda","category-paper","tag-bioinformatics","tag-biology","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-280","tag-sequence-alignment","tag-smith-waterman-algorithm"],"views":1991,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1700","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=1700"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1700\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1700"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1700"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1700"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}