{"id":6266,"date":"2011-11-14T00:30:15","date_gmt":"2011-11-13T22:30:15","guid":{"rendered":"http:\/\/hgpu.org\/?p=6266"},"modified":"2011-11-14T00:30:15","modified_gmt":"2011-11-13T22:30:15","slug":"b-calm-an-open-source-gpu-based-3d-fdtd-with-multi-pole-dispersion-for-plasmonics","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6266","title":{"rendered":"B-CALM: An open-source GPU-based 3D-FDTD with multi-pole dispersion for plasmonics"},"content":{"rendered":"<p>Numerical calculations with finite-difference time-domain (FDTD) on metallic nanostructures in a broad optical spectrum require an accurate approximation of the permittivity of dispersive materials. Here, we present the algorithms behind B-CALM (Belgium-California Light Machine), an open-source 3D-FDTD solver operating on Graphical Processing Units (GPU&#8217;s) with multi-pole dispersion models. Our modified architecture shows a reduction in computational times for multi-pole dispersion models for a broad spectral range. We benchmark B-CALM by computing the absorption efficiency of a metallic nanosphere with a one-pole and a three-poles Drude-Lorentz model and compare it with Mie theory.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Numerical calculations with finite-difference time-domain (FDTD) on metallic nanostructures in a broad optical spectrum require an accurate approximation of the permittivity of dispersive materials. Here, we present the algorithms behind B-CALM (Belgium-California Light Machine), an open-source 3D-FDTD solver operating on Graphical Processing Units (GPU&#8217;s) with multi-pole dispersion models. Our modified architecture shows a reduction in [&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":[36,3,12],"tags":[1787,323,322,359,20,321,176,1783,199],"class_list":["post-6266","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-paper","category-physics","tag-algorithms","tag-fdtd","tag-finite-difference-time-domain","tag-mesoscale-and-nanoscale-physics","tag-nvidia","tag-optics","tag-package","tag-physics","tag-tesla-c1060"],"views":2910,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6266","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=6266"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6266\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6266"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6266"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6266"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}