{"id":1288,"date":"2010-11-08T11:22:13","date_gmt":"2010-11-08T11:22:13","guid":{"rendered":"http:\/\/hgpu.org\/?p=1288"},"modified":"2010-11-08T11:22:13","modified_gmt":"2010-11-08T11:22:13","slug":"towards-dense-linear-algebra-for-hybrid-gpu-accelerated-manycore-systems","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1288","title":{"rendered":"Towards Dense Linear Algebra for Hybrid GPU Accelerated Manycore Systems"},"content":{"rendered":"<p>We highlight the trends leading to the increased appeal of using hybrid multicore+GPU systems for high performance computing. We present a set of techniques that can be used to develop efficient dense linear algebra algorithms for these systems. We illustrate the main ideas with the development of a hybrid LU factorization algorithm where we split the computation over a multicore and a graphics processor, and use particular techniques to reduce the amount of pivoting and communication between the hybrid components. This results in an efficient algorithm with balanced use of a multicore processor and a graphics processor.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We highlight the trends leading to the increased appeal of using hybrid multicore+GPU systems for high performance computing. We present a set of techniques that can be used to develop efficient dense linear algebra algorithms for these systems. We illustrate the main ideas with the development of a hybrid LU factorization algorithm where we split [&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":[1782,14,555,37,20,234],"class_list":["post-1288","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-hybrid-computing","tag-linear-algebra","tag-nvidia","tag-nvidia-geforce-gtx-280"],"views":2557,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1288","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=1288"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1288\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1288"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1288"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1288"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}