{"id":8112,"date":"2012-08-26T20:51:41","date_gmt":"2012-08-26T17:51:41","guid":{"rendered":"http:\/\/hgpu.org\/?p=8112"},"modified":"2012-08-26T20:51:41","modified_gmt":"2012-08-26T17:51:41","slug":"gpu-accelerated-nonlinear-optimization-in-radio-interferometric-calibration","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8112","title":{"rendered":"GPU Accelerated Nonlinear Optimization in Radio Interferometric Calibration"},"content":{"rendered":"<p>We present the GPU based acceleration of two well known nonlinear optimization routines: Levenberg-Marquardt (LM) and Limited Memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) in radio interferometric calibration. Radio interferometric calibration is a heavily compute intensive operation where the same nonlinear optimization problem has to be solved over many time intervals, with different data. We achieve a speedup of about 3 times compared with conventional multi-core CPU based optimization by using GPU accelerated linear algebra routines (CULAtools,CUBLAS). We present details of our GPU accelerated optimization algorithms as well as timing comparisons with non-GPU based multi-core CPU routines.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present the GPU based acceleration of two well known nonlinear optimization routines: Levenberg-Marquardt (LM) and Limited Memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) in radio interferometric calibration. Radio interferometric calibration is a heavily compute intensive operation where the same nonlinear optimization problem has to be solved over many time intervals, with different data. We achieve a speedup of [&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":[36,96,89,3],"tags":[1787,1794,238,14,37,342,20,298,913],"class_list":["post-8112","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-astrophysics","category-nvidia-cuda","category-paper","tag-algorithms","tag-astrophysics","tag-cublas","tag-cuda","tag-linear-algebra","tag-nonlinear-optimization","tag-nvidia","tag-optimization","tag-tesla-m1060"],"views":3890,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8112","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=8112"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8112\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8112"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8112"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8112"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}