{"id":4794,"date":"2011-07-17T17:06:01","date_gmt":"2011-07-17T14:06:01","guid":{"rendered":"http:\/\/hgpu.org\/?p=4794"},"modified":"2011-07-17T17:06:01","modified_gmt":"2011-07-17T14:06:01","slug":"real-time-s-mrtd-simulation-of-electrically-large-indoor-wireless-channels-with-commodity-gpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=4794","title":{"rendered":"Real-time S-MRTD simulation of electrically large indoor wireless channels with commodity GPUs"},"content":{"rendered":"<p>Asymptotic and statistical models have been the only practical means, in terms of cost, performance and accuracy, for simulating electrically large environments. We show, in practice, how the combination of commodity graphics processing units (GPUs), and higher-order scaling function based multi-resolution time-domain (S-MRTD) techniques realize an unprecedented high-fidelity full-wave simulator that is orders of magnitude faster (134x) than otherwise previously possible<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Asymptotic and statistical models have been the only practical means, in terms of cost, performance and accuracy, for simulating electrically large environments. We show, in practice, how the combination of commodity graphics processing units (GPUs), and higher-order scaling function based multi-resolution time-domain (S-MRTD) techniques realize an unprecedented high-fidelity full-wave simulator that is orders of magnitude [&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":[1802,20,385],"class_list":["post-4794","post","type-post","status-publish","format-standard","hentry","category-electrodynamics","category-paper","tag-electrodynamics","tag-nvidia","tag-nvidia-geforce-6800-ultra"],"views":2050,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4794","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=4794"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4794\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4794"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4794"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4794"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}