{"id":3687,"date":"2011-04-23T19:40:29","date_gmt":"2011-04-23T19:40:29","guid":{"rendered":"http:\/\/hgpu.org\/?p=3687"},"modified":"2011-04-23T19:40:29","modified_gmt":"2011-04-23T19:40:29","slug":"introduction-to-gpu-computing-and-cuda-programming-a-case-study-on-fdtd-em-programmers-notebook","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3687","title":{"rendered":"Introduction to GPU Computing and CUDA Programming: A Case Study on FDTD [EM Programmer&#8217;s Notebook]"},"content":{"rendered":"<p>The recent advent of general-purpose graphics-processing units (GPGPUs) as inexpensive arithmetic-processing units brings a relevant amount of computing power to modern desktop PCs. This thus providing an interesting pathway to the acceleration of several numerical electromagnetic methods. In this paper, we explain how to exploit GPGPU features by examining how the computational time of the Finite-Difference Time-Domain Method can be reduced. The attainable efficiency is demonstrated by providing numerical results achieved on a two-dimensional study of a human-antenna interaction problem.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The recent advent of general-purpose graphics-processing units (GPGPUs) as inexpensive arithmetic-processing units brings a relevant amount of computing power to modern desktop PCs. This thus providing an interesting pathway to the acceleration of several numerical electromagnetic methods. In this paper, we explain how to exploit GPGPU features by examining how the computational time of 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":[89,319,3],"tags":[14,1802,323,322,20],"class_list":["post-3687","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-electrodynamics","category-paper","tag-cuda","tag-electrodynamics","tag-fdtd","tag-finite-difference-time-domain","tag-nvidia"],"views":1917,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3687","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=3687"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3687\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3687"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3687"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3687"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}