{"id":3332,"date":"2011-03-24T21:00:28","date_gmt":"2011-03-24T21:00:28","guid":{"rendered":"http:\/\/hgpu.org\/?p=3332"},"modified":"2011-03-24T21:00:28","modified_gmt":"2011-03-24T21:00:28","slug":"accelerating-simulations-of-light-scattering-based-on-finite-difference-time-domain-method-with-general-purpose-gpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3332","title":{"rendered":"Accelerating Simulations of Light Scattering Based on Finite-Difference Time-Domain Method with General Purpose GPUs"},"content":{"rendered":"<p>Simulations of light scattering from nano-structured surface areas require substantial amount of computing time. The emergence of General Purpose Graphics Processing Units (GPGPUs) as affordable PC SIMD arithmetic coprocessors brings the necessary computing power to modern desktop PCs. In this paper we examine how the computation time of the Finite-Difference Time-Domain (FDTD), a classic numerical method for computing a solution to Maxwell&#8217;s equations, can be reduced by leveraging the massively parallel architecture of GPGPUs cards.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Simulations of light scattering from nano-structured surface areas require substantial amount of computing time. The emergence of General Purpose Graphics Processing Units (GPGPUs) as affordable PC SIMD arithmetic coprocessors brings the necessary computing power to modern desktop PCs. In this paper we examine how the computation time of the Finite-Difference Time-Domain (FDTD), a classic numerical [&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":[1787,14,323,322,20,321,1783],"class_list":["post-3332","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-paper","category-physics","tag-algorithms","tag-cuda","tag-fdtd","tag-finite-difference-time-domain","tag-nvidia","tag-optics","tag-physics"],"views":1863,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3332","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=3332"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3332\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3332"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3332"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3332"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}