{"id":3329,"date":"2011-03-24T21:00:24","date_gmt":"2011-03-24T21:00:24","guid":{"rendered":"http:\/\/hgpu.org\/?p=3329"},"modified":"2011-03-24T21:00:24","modified_gmt":"2011-03-24T21:00:24","slug":"a-novel-scheme-for-high-performance-finite-difference-time-domain-fdtd-computations-based-on-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3329","title":{"rendered":"A Novel Scheme for High Performance Finite-Difference Time-Domain (FDTD) Computations Based on GPU"},"content":{"rendered":"<p>Finite-Difference Time-Domain (FDTD) has been proved to be a very useful computational electromagnetic algorithm. However, the scheme based on traditional general purpose processors can be computationally prohibitive and require thousands of CPU hours, which hinders the large-scale application of FDTD. With rapid progress on GPU hardware capability and its programmability, we propose in this paper a novel scheme in which GPU is applied to accelerate three-dimensional FDTD with UPML absorbing boundary conditions. This GPU-based scheme can reduce the computation time significantly, while obtaining high accuracy as compared with the CPU-based scheme. With only one AMD ATI HD4850 GPU, when computational domain is up to (180x180x180), our implementation of the GPU-based FDTD performs approximately 93 times faster than the one running with Intel E2180 dual cores CPU.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Finite-Difference Time-Domain (FDTD) has been proved to be a very useful computational electromagnetic algorithm. However, the scheme based on traditional general purpose processors can be computationally prohibitive and require thousands of CPU hours, which hinders the large-scale application of FDTD. With rapid progress on GPU hardware capability and its programmability, we propose in this paper [&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":[88,319,3],"tags":[7,417,1792,1802,323,322],"class_list":["post-3329","post","type-post","status-publish","format-standard","hentry","category-ati-stream","category-electrodynamics","category-paper","tag-ati","tag-ati-radeon-hd-4850","tag-ati-stream","tag-electrodynamics","tag-fdtd","tag-finite-difference-time-domain"],"views":2741,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3329","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=3329"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3329\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3329"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3329"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3329"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}