{"id":4707,"date":"2011-07-08T14:53:23","date_gmt":"2011-07-08T14:53:23","guid":{"rendered":"http:\/\/hgpu.org\/?p=4707"},"modified":"2011-07-08T14:53:23","modified_gmt":"2011-07-08T14:53:23","slug":"acceleration-of-the-3d-adi-fdtd-method-using-graphics-processor-units","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=4707","title":{"rendered":"Acceleration of the 3D ADI-FDTD method using graphics processor units"},"content":{"rendered":"<p>We present preliminary results of the acceleration of the three-dimensional (3D) alternating direction implicit finite-difference time-domain (ADI-FDTD) method on graphics processor units (GPUs). Although the ADI-FDTD iteration comprises two substeps, which each require solving a tridiagonal matrix system of equations over xy, xz, yz planes of the domain, the application of this scheme frees the time-step size from the Courant-Friedrichs-Lewy stability constraint. In our implementation we took advantage of the fine-grain thread parallelism and coarse-grained block parallelism of the GPU architecture, allowing us to use the parallel cyclic reduction method to simultaneously solve many tridiagonal systems of equations. We obtained a satisfactory speedup of the method indicating that GPUs represent an inexpensive source of computational power for accelerated ADI-FDTD simulations.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present preliminary results of the acceleration of the three-dimensional (3D) alternating direction implicit finite-difference time-domain (ADI-FDTD) method on graphics processor units (GPUs). Although the ADI-FDTD iteration comprises two substeps, which each require solving a tridiagonal matrix system of equations over xy, xz, yz planes of the domain, the application of this scheme frees the [&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":[319,3],"tags":[1802,323,322],"class_list":["post-4707","post","type-post","status-publish","format-standard","hentry","category-electrodynamics","category-paper","tag-electrodynamics","tag-fdtd","tag-finite-difference-time-domain"],"views":1959,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4707","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=4707"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4707\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4707"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4707"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4707"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}