Heterogeneous nodes composed of a multicore CPU and at least one graphics processing unit (GPU) are increasingly common in high-performance scientific computing, and significant programming effort is currently being undertaken to port existing scientific algorithms to these unique architectures. We present implementations for two many-body quantum chemistry methods on heterogeneous nodes: the coupled-cluster with single and double excitations (CCSD) and time-dependent configuration interaction with single and double excitations (TD-CISD) methods. Both methods can be implemented on a computer as a series of dense matrix-matrix multiplications, operations that GPUs are particularly adept at performing. The GPU-accelerated CCSD algorithm is as much as 4.3 times faster than the corresponding CPU algorithm and 9.7 times faster than the algorithm in the Molpro package. The TD-CISD algorithm is accelerated by as much as a factor of 3.9 by GPUs. Enhanced performance is achieved by overlapping CPU and GPU computations.<\/p>\n","protected":false},"excerpt":{"rendered":"
Heterogeneous nodes composed of a multicore CPU and at least one graphics processing unit (GPU) are increasingly common in high-performance scientific computing, and significant programming effort is currently being undertaken to port existing scientific algorithms to these unique architectures. We present implementations for two many-body quantum chemistry methods on heterogeneous nodes: the coupled-cluster with single […]<\/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":[36,66,89,3],"tags":[1787,1790,165,14,452,324,264,20,378],"class_list":["post-6243","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-chemistry","category-nvidia-cuda","category-paper","tag-algorithms","tag-chemistry","tag-computational-chemistry","tag-cuda","tag-heterogeneous-systems","tag-matrix-multiplication","tag-molecular-modeling","tag-nvidia","tag-tesla-c2050"],"views":2498,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6243","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=6243"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6243\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6243"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6243"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6243"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}