The recent multi\/many-core CPUs or GPUs have provided an ideal parallel computing platform to accelerate the time-consuming analysis of radio-frequency\/millimeter-wave (RF\/MM) integrated circuit (IC). This paper develops a structured shooting algorithm that can fully take advantage of parallelism in periodic steady state (PSS) analysis. Utilizing periodic structure of the state matrix of RF\/MM-IC simulation, a cyclic-block-structured shooting-Newton method has been parallelized and mapped onto recent GPU platforms. We first present the formulation of the parallel cyclic-block-structured shooting-Newton algorithm, called periodic Arnoldi shooting method. Then we will present its parallel implementation details on GPU. Results from several industrial examples show that the structured parallel shooting-Newton method on Tesla’s GPU can lead to speedups of more than 20x compared to the state-of-the-art implicit GMRES methods under the same accuracy on the CPU.<\/p>\n","protected":false},"excerpt":{"rendered":"
The recent multi\/many-core CPUs or GPUs have provided an ideal parallel computing platform to accelerate the time-consuming analysis of radio-frequency\/millimeter-wave (RF\/MM) integrated circuit (IC). This paper develops a structured shooting algorithm that can fully take advantage of parallelism in periodic steady state (PSS) analysis. Utilizing periodic structure of the state matrix of RF\/MM-IC simulation, a […]<\/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":[36,11,89,3],"tags":[1787,596,1782,14,628,20,67,199],"class_list":["post-5308","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-cad","tag-computer-science","tag-cuda","tag-numerical-analysis","tag-nvidia","tag-performance","tag-tesla-c1060"],"views":1925,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5308","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=5308"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5308\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5308"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5308"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5308"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}