The Sources Reconstruction Method (SRM) is a non-invasive technique for, among other applications, antenna characterization. The SRM is based on obtaining a distribution of equivalent currents that radiate the same field as the antenna under test. The computation of these currents requires solving a linear system, usually ill-posed, that may be very computationally demanding for commercial antennas. Graphics Processing Units (GPUs) are an interesting hardware choice for solving compute-bound problems that are prone to parallelism. In this paper, we present an implementation on GPUs of the SRM applied to antenna characterization that is based on a compute-bound algorithm with a high degree of parallelism. The GPU implementation introduced in this work provides a dramatic reduction on the time cost compared to our CPU implementation and, in addition, keeps the low-memory footprint of the latter. For the sake of illustration, the equivalent currents are obtained on a base station antenna array and a helix antenna working at practical frequencies. Quasi real-time results are obtained on a desktop workstation.<\/p>\n","protected":false},"excerpt":{"rendered":"
The Sources Reconstruction Method (SRM) is a non-invasive technique for, among other applications, antenna characterization. The SRM is based on obtaining a distribution of equivalent currents that radiate the same field as the antenna under test. The computation of these currents requires solving a linear system, usually ill-posed, that may be very computationally demanding for […]<\/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":[36,89,319,3],"tags":[1787,14,1802,20,1015],"class_list":["post-7334","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-electrodynamics","category-paper","tag-algorithms","tag-cuda","tag-electrodynamics","tag-nvidia","tag-nvidia-geforce-gtx-460"],"views":2223,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7334","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=7334"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7334\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7334"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7334"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7334"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}