This paper proposes a fast method for computing the costs of all-pairs shortest paths (APSPs) on the graphics processing unit (GPU). The proposed method is implemented using compute unified device architecture (CUDA), which offers us a development environment for performing general-purpose computation on the GPU. Our method is based on Harish’s iterative algorithm that computes the cost of the single-source shortest path (SSSP) for every source vertex. We present that exploiting task parallelism in the APSP problem allows us to efficiently use on-chip memory in the GPU, reducing the amount of data being transferred from relatively slower off-chip memory. Furthermore, our task parallel scheme is useful to exploit a higher parallelism, increasing the efficiency with highly threaded code. As a result, our method is 3.4-15 times faster than the prior method. Using on-chip memory, our method eliminates approximately 20% of data loads from off-chip memory.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper proposes a fast method for computing the costs of all-pairs shortest paths (APSPs) on the graphics processing unit (GPU). The proposed method is implemented using compute unified device architecture (CUDA), which offers us a development environment for performing general-purpose computation on the GPU. Our method is based on Harish’s iterative algorithm that computes […]<\/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":[36,11,89,3],"tags":[1787,259,1782,14,20,357,442],"class_list":["post-1197","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-all-pairs-distance","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-8800-gts","tag-path-problems"],"views":2686,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1197","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=1197"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1197\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1197"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1197"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1197"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}