{"id":6277,"date":"2011-11-14T14:12:27","date_gmt":"2011-11-14T12:12:27","guid":{"rendered":"http:\/\/hgpu.org\/?p=6277"},"modified":"2011-11-14T14:12:27","modified_gmt":"2011-11-14T12:12:27","slug":"large-scale-plane-wave-pseudopotential-density-functional-theory-calculations-on-gpu-clusters","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6277","title":{"rendered":"Large Scale Plane Wave Pseudopotential Density Functional Theory Calculations on GPU Clusters"},"content":{"rendered":"<p>In this work, we present our implementation of the density functional theory (DFT) plane wave pseudopotential (PWP) calculations on GPU clusters. This GPU version is developed based on a CPU DFT-PWP code: PEtot, which can calculate up to a thousand atoms on thousands of processors.  Our test indicates that the GPU version can have a ~10 times speed-up over the CPU version. A detail analysis of the speed-up and the scaling on the number of CPU\/GPU computing units (up to 256) are presented. The success of our speed-up relies on the adoption a hybrid reciprocal-space and band-index parallelization scheme. As far as we know, this is the first GPU DFT-PWP code scalable to large number of CPU\/GPU computing units. We also outlined the future work, and what is needed to further increase the computational speed by another factor of 10.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this work, we present our implementation of the density functional theory (DFT) plane wave pseudopotential (PWP) calculations on GPU clusters. This GPU version is developed based on a CPU DFT-PWP code: PEtot, which can calculate up to a thousand atoms on thousands of processors. Our test indicates that the GPU version can have a [&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":[36,89,3,12],"tags":[1787,165,14,106,242,20,67,1783,378],"class_list":["post-6277","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-paper","category-physics","tag-algorithms","tag-computational-chemistry","tag-cuda","tag-gpu-cluster","tag-mpi","tag-nvidia","tag-performance","tag-physics","tag-tesla-c2050"],"views":3774,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6277","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=6277"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6277\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6277"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6277"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6277"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}