{"id":5770,"date":"2011-10-03T17:14:08","date_gmt":"2011-10-03T14:14:08","guid":{"rendered":"http:\/\/hgpu.org\/?p=5770"},"modified":"2011-10-03T17:14:08","modified_gmt":"2011-10-03T14:14:08","slug":"gpu-accelerated-dna-distance-matrix-computation","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5770","title":{"rendered":"GPU-Accelerated DNA Distance Matrix Computation"},"content":{"rendered":"<p>Distance matrix calculation used in phylogeny analysis is computational intensive. The growing sequences data sets necessitate fast computation method. This paper accelerate Felsenstein&#8217;s DNADIST program by using OpenCL to exploit the great computation capability of graphic card. The GPUaccelerated DNADIST program achieves more than 12-fold speedup over the serial CPU program on a personal workstation with a 2.66GHz quad-core Intel CPU and an AMD HD5850 graphics card. And dual HD5850 cards on the same platform perform linear improvement of 24-fold speedup. The program also shows good performance portability by achieving 16-fold speedup with a NVIDIA Tesla C2050 card.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Distance matrix calculation used in phylogeny analysis is computational intensive. The growing sequences data sets necessitate fast computation method. This paper accelerate Felsenstein&#8217;s DNADIST program by using OpenCL to exploit the great computation capability of graphic card. The GPUaccelerated DNADIST program achieves more than 12-fold speedup over the serial CPU program on a personal workstation [&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":[10,90,3],"tags":[7,642,123,1781,525,20,1793,67,378],"class_list":["post-5770","post","type-post","status-publish","format-standard","hentry","category-biology","category-opencl","category-paper","tag-ati","tag-ati-radeon-hd-5850","tag-bioinformatics","tag-biology","tag-genetics","tag-nvidia","tag-opencl","tag-performance","tag-tesla-c2050"],"views":2058,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5770","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=5770"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5770\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5770"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5770"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5770"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}