{"id":2939,"date":"2011-02-22T14:02:40","date_gmt":"2011-02-22T14:02:40","guid":{"rendered":"http:\/\/hgpu.org\/?p=2939"},"modified":"2011-02-22T14:02:40","modified_gmt":"2011-02-22T14:02:40","slug":"energy-efficiency-of-mixed-precision-iterative-refinement-methods-using-hybrid-hardware-platforms","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2939","title":{"rendered":"Energy efficiency of mixed precision iterative refinement methods using hybrid hardware platforms"},"content":{"rendered":"<p>In this paper we evaluate the possibility of using mixed precision algorithms on different hardware platforms to obtain energy-efficient solvers for linear systems of equations. Our test-cases arise in the context of computational fluid dynamics. Therefore, we analyze the energy efficiency of common cluster nodes and a hybrid, GPU-accelerated cluster node, when applying a linear solver, that can benefit from the use of different precision formats. We show the high potential of hardware-aware computing in terms of performance and energy efficiency.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper we evaluate the possibility of using mixed precision algorithms on different hardware platforms to obtain energy-efficient solvers for linear systems of equations. Our test-cases arise in the context of computational fluid dynamics. Therefore, we analyze the energy efficiency of common cluster nodes and a hybrid, GPU-accelerated cluster node, when applying a linear [&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":[1782,344,37,625,20,199,244],"class_list":["post-2939","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-computer-science","tag-energy-efficient-computing","tag-linear-algebra","tag-mixed-precision","tag-nvidia","tag-tesla-c1060","tag-tesla-s1070"],"views":1999,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2939","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=2939"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2939\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2939"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2939"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2939"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}