{"id":17734,"date":"2017-10-31T18:12:16","date_gmt":"2017-10-31T16:12:16","guid":{"rendered":"https:\/\/hgpu.org\/?p=17734"},"modified":"2017-10-31T18:12:16","modified_gmt":"2017-10-31T16:12:16","slug":"an-efficient-gpu-algorithm-for-tetrahedron-based-brillouin-zone-integration","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=17734","title":{"rendered":"An efficient GPU algorithm for tetrahedron-based Brillouin-zone integration"},"content":{"rendered":"<p>We report an efficient algorithm for calculating momentum-space integrals in solid state systems on modern graphics processing units (GPUs). We extend the tetrahedron method by Bl&quot;ochl et al.~to the more general case of the integration of a momentum as well as energy dependent quantity and implement the algorithm based on the CUDA programming framework. We test this method by applying it to a simple example, the calculation of the orbital-resolved density of states. We benchmark our code on the problem of calculating the orbital-resolved density of states in an iron-based superconductor and discuss the design choices made in the implementation. Our algorithm delivers large speedups of up to a factor $sim165$ also for moderately sized workloads compared to standard algorithms executed on central processing units (CPUs).<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We report an efficient algorithm for calculating momentum-space integrals in solid state systems on modern graphics processing units (GPUs). We extend the tetrahedron method by Bl&quot;ochl et al.~to the more general case of the integration of a momentum as well as energy dependent quantity and implement the algorithm based on the CUDA programming framework. We [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,89,3,12],"tags":[1787,98,14,20,1779,1783],"class_list":["post-17734","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-paper","category-physics","tag-algorithms","tag-computational-physics","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-970","tag-physics"],"views":4804,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/17734","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=17734"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/17734\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=17734"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=17734"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=17734"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}