{"id":12474,"date":"2014-07-11T23:38:23","date_gmt":"2014-07-11T20:38:23","guid":{"rendered":"http:\/\/hgpu.org\/?p=12474"},"modified":"2014-07-11T23:38:23","modified_gmt":"2014-07-11T20:38:23","slug":"development-of-a-restricted-additive-schwarz-preconditioner-for-sparse-linear-systems-on-nvidia-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=12474","title":{"rendered":"Development of a Restricted Additive Schwarz Preconditioner for Sparse Linear Systems on NVIDIA GPU"},"content":{"rendered":"<p>In this paper, we develop, study and implement a restricted additive Schwarz (RAS) preconditioner for speedup of the solution of sparse linear systems on NVIDIA Tesla GPU. A novel algorithm for constructing this preconditioner is proposed. This algorithm involves two phases. In the first phase, the construction of the RAS preconditioner is transformed to an incomplete-LU problem. In the second phase, a parallel triangular solver is developed and the incomplete-LU problem is solved by this solver. Numerical experiments show that the speedup of this preconditioner is sufficiently high.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we develop, study and implement a restricted additive Schwarz (RAS) preconditioner for speedup of the solution of sparse linear systems on NVIDIA Tesla GPU. A novel algorithm for constructing this preconditioner is proposed. This algorithm involves two phases. In the first phase, the construction of the RAS preconditioner is transformed to an [&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":[36,11,89,3],"tags":[1787,1782,14,37,20,593,421,378,1006],"class_list":["post-12474","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-linear-algebra","tag-nvidia","tag-sparse-linear-iterative-solvers","tag-sparse-matrix","tag-tesla-c2050","tag-tesla-c2070"],"views":2299,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12474","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=12474"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12474\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12474"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12474"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12474"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}