{"id":3223,"date":"2011-03-16T16:43:26","date_gmt":"2011-03-16T16:43:26","guid":{"rendered":"http:\/\/hgpu.org\/?p=3223"},"modified":"2011-03-16T16:43:26","modified_gmt":"2011-03-16T16:43:26","slug":"implementing-the-himeno-benchmark-with-cuda-on-gpu-clusters","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3223","title":{"rendered":"Implementing the Himeno benchmark with CUDA on GPU clusters"},"content":{"rendered":"<p>This paper describes the use of CUDA to accelerate the Himeno benchmark on clusters with GPUs. The implementation is designed to optimize memory bandwidth utilization. Our approach achieves over 83% of the theoretical peak bandwidth on a NVIDIA Tesla C1060 GPU and performs at over 50 GFlops. A multi-GPU implementation that utilizes MPI alongside CUDA streams to overlap GPU execution with data transfers allows linear scaling and performs at over 800 GFlops on a cluster with 16 GPUs. The paper presents the optimizations required to achieve this level of performance.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper describes the use of CUDA to accelerate the Himeno benchmark on clusters with GPUs. The implementation is designed to optimize memory bandwidth utilization. Our approach achieves over 83% of the theoretical peak bandwidth on a NVIDIA Tesla C1060 GPU and performs at over 50 GFlops. A multi-GPU implementation that utilizes MPI alongside CUDA [&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":[451,1782,14,106,20,67,199],"class_list":["post-3223","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-benchmarking","tag-computer-science","tag-cuda","tag-gpu-cluster","tag-nvidia","tag-performance","tag-tesla-c1060"],"views":2500,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3223","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=3223"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3223\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3223"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3223"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3223"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}