{"id":6580,"date":"2011-12-14T17:30:09","date_gmt":"2011-12-14T15:30:09","guid":{"rendered":"http:\/\/hgpu.org\/?p=6580"},"modified":"2011-12-14T17:30:09","modified_gmt":"2011-12-14T15:30:09","slug":"power-consumption-of-mixed-precision-in-the-iterative-solution-of-sparse-linear-systems","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6580","title":{"rendered":"Power consumption of mixed precision in the iterative solution of sparse linear systems"},"content":{"rendered":"<p>This paper presents a detailed analysis of a mixed precision iterative refinement solver applied to a linear system obtained from the 2D discretization of a fluid flow problem. The total execution time and energy need of different soft- and hardware implementations are measured and compared with those of a plain GMRES-based solver in double precision. The time and energy consumption of individual parts of the algorithm are monitored as well, enabling a deeper insight and the possibility of optimizing the energy consumption of the code on a general-purpose multi-core architecture and systems accelerated by a graphics processor.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents a detailed analysis of a mixed precision iterative refinement solver applied to a linear system obtained from the 2D discretization of a fluid flow problem. The total execution time and energy need of different soft- and hardware implementations are measured and compared with those of a plain GMRES-based solver in double precision. [&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":[36,11,89,3],"tags":[1787,1782,14,344,625,20,199],"class_list":["post-6580","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-energy-efficient-computing","tag-mixed-precision","tag-nvidia","tag-tesla-c1060"],"views":1958,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6580","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=6580"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6580\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6580"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6580"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6580"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}