{"id":2015,"date":"2010-12-12T21:37:18","date_gmt":"2010-12-12T21:37:18","guid":{"rendered":"http:\/\/hgpu.org\/?p=2015"},"modified":"2010-12-12T21:37:18","modified_gmt":"2010-12-12T21:37:18","slug":"swps3-fast-multi-threaded-vectorized-smith-waterman-for-ibm-cellb-e-and-x86sse2","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2015","title":{"rendered":"SWPS3 &#8211; fast multi-threaded vectorized Smith-Waterman for IBM Cell\/B.E. and x86\/SSE2"},"content":{"rendered":"<p>BACKGROUND:We present SWPS3, a vectorized implementation of the Smith-Waterman local alignment algorithm optimized for both the Cell\/B.E. and x86 architectures. The paper describes SWPS3 and compares its performances with several other implementations. FINDINGS:Our benchmarking results show that SWPS3 is currently the fastest implementation of a vectorized Smith-Waterman on the Cell\/B.E., outperforming the only other known implementation by a factor of at least 4: on a Playstation 3, it achieves up to 8.0 billion cell-updates per second (GCUPS). Using the SSE2 instruction set, a quad-core Intel Pentium can reach 15.7 GCUPS. We also show that SWPS3 on this CPU is faster than a recent GPU implementation. Finally, we note that under some circumstances, alignments are computed at roughly the same speed as BLAST, a heuristic method.CONCLUSIONS:The Cell\/B.E. can be a powerful platform to align biological sequences. Besides, the performance gap between exact and heuristic methods has almost disappeared, especially for long protein sequences.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>BACKGROUND:We present SWPS3, a vectorized implementation of the Smith-Waterman local alignment algorithm optimized for both the Cell\/B.E. and x86 architectures. The paper describes SWPS3 and compares its performances with several other implementations. FINDINGS:Our benchmarking results show that SWPS3 is currently the fastest implementation of a vectorized Smith-Waterman on the Cell\/B.E., outperforming the only other known [&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":[1781,545,14,20,183,298,176,209,284],"class_list":["post-2015","post","type-post","status-publish","format-standard","hentry","category-biology","category-nvidia-cuda","category-paper","tag-biology","tag-cell-processor","tag-cuda","tag-nvidia","tag-nvidia-geforce-8800-gtx","tag-optimization","tag-package","tag-sequence-alignment","tag-smith-waterman-algorithm"],"views":2489,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2015","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=2015"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2015\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2015"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2015"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2015"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}