{"id":15140,"date":"2015-12-22T01:52:49","date_gmt":"2015-12-21T23:52:49","guid":{"rendered":"http:\/\/hgpu.org\/?p=15140"},"modified":"2015-12-22T01:52:49","modified_gmt":"2015-12-21T23:52:49","slug":"parallel-fdtd-arithmetic-simulation-based-on-distributed-heterogeneous-cluster-system","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=15140","title":{"rendered":"Parallel FDTD Arithmetic Simulation Based on Distributed Heterogeneous Cluster System"},"content":{"rendered":"<p>This paper puts forward a new FDTD parallel algorithm, which is developed based on the distributed platform, the algorithm was debugged in Shanghai Jiao-tong University for the high performance computing center GPU cluster, &quot;Rubik&#8217;s Cube&quot; commercial super computer at Shanghai Supercomputer Center and &quot;divinity blue&quot; domestic super computer platform at the National Supercomputing Center in Ji&#8217;nan. By pure CPU, GPU and CPU and GPU hybrid test, thread scheduling level and the kernel function processing speed have been improved significantly, while reducing the proportion of the time of communication and improving the acceleration ratio and operation efficiency. Finally, the topology optimization of the model is verified by 2 * 2 micro-strip arrays. The results show that the algorithm is accurate and effective.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper puts forward a new FDTD parallel algorithm, which is developed based on the distributed platform, the algorithm was debugged in Shanghai Jiao-tong University for the high performance computing center GPU cluster, &quot;Rubik&#8217;s Cube&quot; commercial super computer at Shanghai Supercomputer Center and &quot;divinity blue&quot; domestic super computer platform at the National Supercomputing Center in [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,319,90,3,12],"tags":[1782,1802,323,322,106,452,242,20,1793,1783,1390,1017],"class_list":["post-15140","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-electrodynamics","category-opencl","category-paper","category-physics","tag-computer-science","tag-electrodynamics","tag-fdtd","tag-finite-difference-time-domain","tag-gpu-cluster","tag-heterogeneous-systems","tag-mpi","tag-nvidia","tag-opencl","tag-physics","tag-tesla-k20","tag-tesla-m2070"],"views":2876,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/15140","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=15140"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/15140\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=15140"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=15140"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=15140"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}