{"id":6284,"date":"2011-11-15T17:52:29","date_gmt":"2011-11-15T15:52:29","guid":{"rendered":"http:\/\/hgpu.org\/?p=6284"},"modified":"2011-11-15T17:52:29","modified_gmt":"2011-11-15T15:52:29","slug":"design-of-milc-lattice-qcd-application-for-gpu-clusters","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6284","title":{"rendered":"Design of MILC Lattice QCD Application for GPU Clusters"},"content":{"rendered":"<p>We present an implementation of the improved staggered quark action lattice QCD computation designed for execution on a GPU cluster. The parallelization strategy is based on dividing the space-time lattice along the time dimension and distributing the sub-lattices among the GPU cluster nodes. We provide a mixed-precision floating-point GPU implementation of the multi-mass conjugate gradient solver. Our single GPU implementation of the conjugate gradient solver achieves a 9x performance improvement over the highly optimized code executed on a state-of-the-art eight-core CPU node. The overall application executes almost six times faster on a GPU-enabled cluster vs. a conventional multi-core cluster. The developed code is currently used for running production QCD calculations with electromagnetic corrections.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present an implementation of the improved staggered quark action lattice QCD computation designed for execution on a GPU cluster. The parallelization strategy is based on dividing the space-time lattice along the time dimension and distributing the sub-lattices among the GPU cluster nodes. We provide a mixed-precision floating-point GPU implementation of the multi-mass conjugate gradient [&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":[580,14,106,110,100,20,680,1783,335,199,378],"class_list":["post-6284","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-paper","category-physics","tag-conjugate-gradient-solver","tag-cuda","tag-gpu-cluster","tag-high-energy-physics-lattice","tag-high-energy-physics-phenomenology","tag-nvidia","tag-openmpi","tag-physics","tag-qcd","tag-tesla-c1060","tag-tesla-c2050"],"views":2255,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6284","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=6284"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6284\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6284"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6284"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6284"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}