{"id":6202,"date":"2011-11-08T13:12:06","date_gmt":"2011-11-08T11:12:06","guid":{"rendered":"http:\/\/hgpu.org\/?p=6202"},"modified":"2011-11-08T13:12:06","modified_gmt":"2011-11-08T11:12:06","slug":"performance-analysis-of-a-hybrid-mpicuda-implementation-of-the-naslu-benchmark","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6202","title":{"rendered":"Performance analysis of a hybrid MPI\/CUDA implementation of the NASLU benchmark"},"content":{"rendered":"<p>We present the performance analysis of a port of the LU benchmark from the NAS Parallel Benchmark (NPB) suite to NVIDIA&#8217;s Compute Unified Device Architecture (CUDA), and report on the optimisation efforts employed to take advantage of this platform. Execution times are reported for several different GPUs, ranging from low-end consumergrade products to high-end HPC-grade devices, including the Tesla C2050 built on NVIDIA&#8217;s Fermi processor. We also utilise recently developed performance models of LU to facilitate a comparison between future large-scale distributed clusters of GPU devices and existing clusters built on traditional CPU architectures, including a quad-socket, quad-core AMD Opteron cluster and an IBM BlueGene\/P.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present the performance analysis of a port of the LU benchmark from the NAS Parallel Benchmark (NPB) suite to NVIDIA&#8217;s Compute Unified Device Architecture (CUDA), and report on the optimisation efforts employed to take advantage of this platform. Execution times are reported for several different GPUs, ranging from low-end consumergrade products to high-end HPC-grade [&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":[11,89,3],"tags":[451,1782,14,242,20,674,997,852,67,199,378,429],"class_list":["post-6202","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-benchmarking","tag-computer-science","tag-cuda","tag-mpi","tag-nvidia","tag-nvidia-geforce-8400-gs","tag-nvidia-geforce-9400-gt","tag-operating-systems","tag-performance","tag-tesla-c1060","tag-tesla-c2050","tag-tesla-t10"],"views":2351,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6202","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=6202"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6202\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6202"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6202"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6202"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}