This paper describes the approach and the speedup obtained in performing Smith-Waterman database searches on heterogeneous platforms comprising of multi core CPU and multi GPU systems. Most of the advanced and optimized Smith-Waterman algorithm versions have demonstrated remarkable speedup over NCBI BLAST versions, viz., SWPS3 based on x86 SSE2 instructions and CUDASW++ v2.0 CUDA implementation on GPU. This work proposes a hybrid Smith-Waterman algorithm that integrates the state-of-the art CPU and GPU solutions for accelerating Smith-Waterman algorithm in which GPU acts as a co-processor and shares the workload with the CPU enabling us to realize remarkable performance of over 70 GCUPS resulting from simultaneous CPU-GPU execution. In this work, both CPU and GPU are graded equally in performance for Smith-Waterman rather than previous approaches of porting the computationally intensive portions onto the GPUs or a naive multi-core CPU approach.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper describes the approach and the speedup obtained in performing Smith-Waterman database searches on heterogeneous platforms comprising of multi core CPU and multi GPU systems. Most of the advanced and optimized Smith-Waterman algorithm versions have demonstrated remarkable speedup over NCBI BLAST versions, viz., SWPS3 based on x86 SSE2 instructions and CUDASW++ v2.0 CUDA implementation […]<\/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":[123,1781,14,452,20,284],"class_list":["post-4450","post","type-post","status-publish","format-standard","hentry","category-biology","category-nvidia-cuda","category-paper","tag-bioinformatics","tag-biology","tag-cuda","tag-heterogeneous-systems","tag-nvidia","tag-smith-waterman-algorithm"],"views":2072,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4450","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=4450"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4450\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4450"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4450"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4450"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}