{"id":9354,"date":"2013-05-09T22:31:37","date_gmt":"2013-05-09T19:31:37","guid":{"rendered":"http:\/\/hgpu.org\/?p=9354"},"modified":"2013-05-09T22:31:37","modified_gmt":"2013-05-09T19:31:37","slug":"gpu-sparse-matrix-multiplication-with-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=9354","title":{"rendered":"GPU Sparse Matrix Multiplication with CUDA"},"content":{"rendered":"<p>Matrix multiplication is a commonly-used mathematical operation that has many practical applications. It is used to solve a number of problems in a wide variety of fields including science, engineering, and computer science. Given two matrices, A and B, and a resultant matrix C. The concept of density is used to describe the number of nonzero elements in a matrix relative to the total number of elements. For an NxM matrix with Z nonzero elements, the density is defined as Z=(NxM). A sparse matrix is one which has a low density. Sparse matrices can be stored in special formats to eliminate the need for the zero elements to be stored. The storage format and potentially large matrix size presents a challenge when designing an efficient sparse matrix multiplication algorithm.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Matrix multiplication is a commonly-used mathematical operation that has many practical applications. It is used to solve a number of problems in a wide variety of fields including science, engineering, and computer science. Given two matrices, A and B, and a resultant matrix C. The concept of density is used to describe the number of [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","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":[1787,1782,14,324,20,421,102],"class_list":["post-9354","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-matrix-multiplication","tag-nvidia","tag-sparse-matrix","tag-tutorial"],"views":3303,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9354","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=9354"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9354\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9354"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9354"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9354"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}