{"id":1408,"date":"2010-11-11T13:42:03","date_gmt":"2010-11-11T13:42:03","guid":{"rendered":"http:\/\/hgpu.org\/?p=1408"},"modified":"2010-11-11T13:42:03","modified_gmt":"2010-11-11T13:42:03","slug":"exact-sparse-matrix-vector-multiplication-on-gpus-and-multicore-architectures","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1408","title":{"rendered":"Exact Sparse Matrix-Vector Multiplication on GPU&#8217;s and Multicore Architectures"},"content":{"rendered":"<p>We propose different implementations of the sparse matrix&#8211;dense vector multiplication (spmv{}) for finite fields and rings $Zb\/mZb$. We take advantage of graphic card processors (GPU) and multi-core architectures. Our aim is to improve the speed of spmv{} in the linbox library, and henceforth the speed of its black box algorithms. Besides, we use this and a new parallelization of the sigma-basis algorithm in a parallel block Wiedemann rank implementation over finite fields.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We propose different implementations of the sparse matrix&#8211;dense vector multiplication (spmv{}) for finite fields and rings $Zb\/mZb$. We take advantage of graphic card processors (GPU) and multi-core architectures. Our aim is to improve the speed of spmv{} in the linbox library, and henceforth the speed of its black box algorithms. Besides, we use this and [&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":[1782,14,597,20,234,252,176,421,650],"class_list":["post-1408","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-mathematical-software","tag-nvidia","tag-nvidia-geforce-gtx-280","tag-openmp","tag-package","tag-sparse-matrix","tag-symbolic-computation"],"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\/1408","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=1408"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1408\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1408"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1408"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1408"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}