{"id":12705,"date":"2014-08-23T23:00:36","date_gmt":"2014-08-23T20:00:36","guid":{"rendered":"http:\/\/hgpu.org\/?p=12705"},"modified":"2014-08-23T23:11:01","modified_gmt":"2014-08-23T20:11:01","slug":"structured-orthogonal-inversion-of-block-p-cyclic-matrices-on-multicore-with-gpu-accelerators","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=12705","title":{"rendered":"Structured Orthogonal Inversion of Block p-Cyclic Matrices on Multicore with GPU Accelerators"},"content":{"rendered":"<p>We present a block structured orthogonal factorization (BSOF) algorithm and its parallelization for computing the inversion of block p-cyclic matrices.We aim at the high performance on multicores with GPU accelerators. We provide a quantitative performance model for optimal host-device load balance, and validate the model through numerical tests. Benchmarking results show that the parallel BSOF based inversion algorithm attains up to 90% of DGEMM performance on hybrid CPU+GPU systems.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present a block structured orthogonal factorization (BSOF) algorithm and its parallelization for computing the inversion of block p-cyclic matrices.We aim at the high performance on multicores with GPU accelerators. We provide a quantitative performance model for optimal host-device load balance, and validate the model through numerical tests. Benchmarking results show that the parallel BSOF [&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":[36,11,89,157,3],"tags":[1787,1782,14,288,1796,20,379,176,199],"class_list":["post-12705","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-mathematics","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-factorization","tag-mathematics","tag-nvidia","tag-nvidia-geforce-gtx-480","tag-package","tag-tesla-c1060"],"views":2627,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12705","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=12705"}],"version-history":[{"count":1,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12705\/revisions"}],"predecessor-version":[{"id":12706,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12705\/revisions\/12706"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12705"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12705"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12705"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}