With early hardware limitations of the GPU (lack of synchronization primitives and limited memory caching mechanisms)can make GPU-based computation inefficient, and emerging DNA sequence technologies open up more opportunities for molecular biology. This paper presents the issues of parallel implementation of longest overlap region Problem on a multiprocessor GPU using the Compute Unified Device Architecture (CUDA) platform (Intel(R) Core(TM) i3- 3110m quad-core. Compared to standard CPU implementation, CUDA performance proves the method of longest overlap region recognition of noncoding DNA is an efficient approach to high-performance bioinformatics applications. The study show the fact that the efficiency is more than 15 times than that of CPU serial implementation. We believe our method give a cost-efficient solution to the bioinformatics community for solving longest overlap region recognition problem and other related fields.<\/p>\n","protected":false},"excerpt":{"rendered":"
With early hardware limitations of the GPU (lack of synchronization primitives and limited memory caching mechanisms)can make GPU-based computation inefficient, and emerging DNA sequence technologies open up more opportunities for molecular biology. This paper presents the issues of parallel implementation of longest overlap region Problem on a multiprocessor GPU using the Compute Unified Device Architecture […]<\/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":[36,10,89,3],"tags":[1787,123,1781,14,20,1392],"class_list":["post-11882","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-biology","category-nvidia-cuda","category-paper","tag-algorithms","tag-bioinformatics","tag-biology","tag-cuda","tag-nvidia","tag-nvidia-geforce-gt-610-m"],"views":2198,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11882","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=11882"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11882\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11882"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11882"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11882"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}