{"id":14135,"date":"2015-06-22T22:53:48","date_gmt":"2015-06-22T19:53:48","guid":{"rendered":"http:\/\/hgpu.org\/?p=14135"},"modified":"2015-06-22T22:53:48","modified_gmt":"2015-06-22T19:53:48","slug":"comparative-study-of-the-parallelization-of-the-smith-waterman-algorithm-on-openmp-and-cuda-c","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=14135","title":{"rendered":"Comparative Study of the Parallelization of the Smith-Waterman Algorithm on OpenMP and Cuda C"},"content":{"rendered":"<p>In this paper, we present parallel programming approaches to calculate the values of the cells in matrix&#8217;s scoring used in the Smith-Waterman&#8217;s algorithm for sequence alignment. This algorithm, well known in bioinformatics for its applications, is unfortunately time-consuming on a serial computer. We use formulation based on anti-diagonals structure of data. This representation focuses on parallelizable parts of the algorithm without changing the initial formulation of the algorithm. Approaching data in that way give us a formulation more flexible. To examine this approach, we encode it in OpenMP and Cuda C. The performance obtained shows the interest of our paper.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we present parallel programming approaches to calculate the values of the cells in matrix&#8217;s scoring used in the Smith-Waterman&#8217;s algorithm for sequence alignment. This algorithm, well known in bioinformatics for its applications, is unfortunately time-consuming on a serial computer. We use formulation based on anti-diagonals structure of data. This representation focuses on [&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":[10,89,3],"tags":[123,1781,14,20,1394,252,209,284],"class_list":["post-14135","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-670","tag-openmp","tag-sequence-alignment","tag-smith-waterman-algorithm"],"views":2747,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/14135","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=14135"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/14135\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=14135"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=14135"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=14135"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}