{"id":6458,"date":"2011-12-02T10:49:01","date_gmt":"2011-12-02T08:49:01","guid":{"rendered":"http:\/\/hgpu.org\/?p=6458"},"modified":"2011-12-02T10:49:01","modified_gmt":"2011-12-02T08:49:01","slug":"an-error-correction-solver-for-linear-systems-evaluation-of-mixed-precision-implementations","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6458","title":{"rendered":"An error correction solver for linear systems: Evaluation of mixed precision implementations"},"content":{"rendered":"<p>This paper proposes an error correction method for solving linear systems of equations and the evaluation of an implementation using mixed precision techniques. While different technologies are available, graphic processing units (GPUs) have been established as particularly powerful coprocessors in recent years. For this reason, our error correction approach is focused on a CUDA implementation executing the error correction solver on the GPU. Benchmarks are performed both for artificially created matrices with preset characteristics as well as matrices obtained from finite element discretizations of fluid flow problems.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper proposes an error correction method for solving linear systems of equations and the evaluation of an implementation using mixed precision techniques. While different technologies are available, graphic processing units (GPUs) have been established as particularly powerful coprocessors in recent years. For this reason, our error correction approach is focused on a CUDA implementation [&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":[11,89,3],"tags":[451,1782,14,625,20,244],"class_list":["post-6458","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-benchmarking","tag-computer-science","tag-cuda","tag-mixed-precision","tag-nvidia","tag-tesla-s1070"],"views":1902,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6458","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=6458"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6458\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6458"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6458"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6458"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}