K-shortest path algorithm is generalization of the shortest path algorithm. K-shortest path is used in various fields like sequence alignment problem in molecular bioinformatics, robot motion planning, path finding in gene network where speed to calculate paths plays a vital role. Parallel implementation is one of the best ways to fulfill the requirement of these applications. A GPU based parallel algorithm is developed to find k number of shortest path in a positive edge-weighted directed large graph. In calculated shortest path repetition of the vertices is not allowed. Implemented algorithm calculates a k-shortest path between two pair of vertices of a graph with n nodes and m vertices. This approach is based on Yen’s algorithm to find k-shortest loopless path. We implemented our algorithms in Nvidia’s GPU using Compute Unified Device Architecture (CUDA). This paper presents comparative analysis between CPU and GPU based implementation of Yen’s Algorithm. Our approach achieves the 6 time speed up in comparison of serial algorithm.<\/p>\n","protected":false},"excerpt":{"rendered":"
K-shortest path algorithm is generalization of the shortest path algorithm. K-shortest path is used in various fields like sequence alignment problem in molecular bioinformatics, robot motion planning, path finding in gene network where speed to calculate paths plays a vital role. Parallel implementation is one of the best ways to fulfill the requirement of these […]<\/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":[10,11,89,3],"tags":[123,1781,1782,14,20,209,1226],"class_list":["post-14086","post","type-post","status-publish","format-standard","hentry","category-biology","category-computer-science","category-nvidia-cuda","category-paper","tag-bioinformatics","tag-biology","tag-computer-science","tag-cuda","tag-nvidia","tag-sequence-alignment","tag-tesla-c2075"],"views":3184,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/14086","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=14086"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/14086\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=14086"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=14086"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=14086"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}