{"id":1384,"date":"2010-11-09T20:31:27","date_gmt":"2010-11-09T20:31:27","guid":{"rendered":"http:\/\/hgpu.org\/?p=1384"},"modified":"2010-11-09T20:31:27","modified_gmt":"2010-11-09T20:31:27","slug":"multi-gpu-performance-of-conjugate-gradient-algorithm-with-staggered-fermions","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1384","title":{"rendered":"Multi GPU Performance of Conjugate Gradient Algorithm with Staggered Fermions"},"content":{"rendered":"<p>We report results of the performance test of GPUs obtained using the conjugate gradient (CG) algorithm for staggered fermions on the MILC fine lattice ($28^3 times 96$). We use GPUs of nVIDIA GTX 295 model for the test. When we turn off the MPI communication and use only a single GPU, the performance is 35 giga flops in double precision, which corresponds to 47% of the peak. When we turn on the MPI communication and use multi-GPUs, the performance is reduced down to 12.3 giga flops. The data transfer through the infiniband network and PCI-E bus I\/O is a main bottle neck. We suggest two potential solutions of how to optimize the data transfer.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We report results of the performance test of GPUs obtained using the conjugate gradient (CG) algorithm for staggered fermions on the MILC fine lattice ($28^3 times 96$). We use GPUs of nVIDIA GTX 295 model for the test. When we turn off the MPI communication and use only a single GPU, the performance is 35 [&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":[89,3,12],"tags":[14,106,110,72,242,20,251,436,379,1783,335,378],"class_list":["post-1384","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-paper","category-physics","tag-cuda","tag-gpu-cluster","tag-high-energy-physics-lattice","tag-monte-carlo-simulation","tag-mpi","tag-nvidia","tag-nvidia-geforce-gtx-285","tag-nvidia-geforce-gtx-295","tag-nvidia-geforce-gtx-480","tag-physics","tag-qcd","tag-tesla-c2050"],"views":2245,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1384","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=1384"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1384\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1384"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1384"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1384"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}