{"id":3665,"date":"2011-04-21T09:58:36","date_gmt":"2011-04-21T09:58:36","guid":{"rendered":"http:\/\/hgpu.org\/?p=3665"},"modified":"2011-04-21T09:58:36","modified_gmt":"2011-04-21T09:58:36","slug":"using-graphics-processors-to-accelerate-the-computation-of-the-matrix-inverse","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3665","title":{"rendered":"Using graphics processors to accelerate the computation of the matrix inverse"},"content":{"rendered":"<p>We study the use of massively parallel architectures for computing a matrix inverse. Two different algorithms are reviewed, the traditional approach based on Gaussian elimination and the Gauss-Jordan elimination alternative, and several high performance implementations are presented and evaluated. The target architecture is a current general-purpose multicore processor (CPU) connected to a graphics processor (GPU). Numerical experiments show the efficiency attained by the proposed implementations and how the computation of large-scale inverses, which only a few years ago would have required a distributed-memory cluster, take only a few minutes on a hybrid architecture formed by a multicore CPU and a GPU.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We study the use of massively parallel architectures for computing a matrix inverse. Two different algorithms are reviewed, the traditional approach based on Gaussian elimination and the Gauss-Jordan elimination alternative, and several high performance implementations are presented and evaluated. The target architecture is a current general-purpose multicore processor (CPU) connected to a graphics processor (GPU). [&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":[11,3],"tags":[1782,37,847],"class_list":["post-3665","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-computer-science","tag-linear-algebra","tag-matrix-inversion"],"views":2449,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3665","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=3665"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3665\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3665"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3665"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3665"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}