{"id":2437,"date":"2011-01-11T12:38:25","date_gmt":"2011-01-11T12:38:25","guid":{"rendered":"http:\/\/hgpu.org\/?p=2437"},"modified":"2011-01-11T12:38:25","modified_gmt":"2011-01-11T12:38:25","slug":"accelerating-linpack-with-cuda-on-heterogenous-clusters","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2437","title":{"rendered":"Accelerating linpack with CUDA on heterogenous clusters"},"content":{"rendered":"<p>This paper describes the use of CUDA to accelerate the Linpack benchmark on heterogenous clusters, where both CPUs and GPUs are used in synergy with minor or no modifications to the original source code. A host library intercepts the calls to DGEMM and DTRSM and executes them simultaneously on both GPUs and CPU cores. An 8U cluster is able to sustain more than a Teraflop using a CUDA accelerated version of HPL.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper describes the use of CUDA to accelerate the Linpack benchmark on heterogenous clusters, where both CPUs and GPUs are used in synergy with minor or no modifications to the original source code. A host library intercepts the calls to DGEMM and DTRSM and executes them simultaneously on both GPUs and CPU cores. An [&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":[1782,14,106,37,20],"class_list":["post-2437","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-gpu-cluster","tag-linear-algebra","tag-nvidia"],"views":2525,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2437","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=2437"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2437\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2437"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2437"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2437"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}