{"id":945,"date":"2010-10-27T17:56:09","date_gmt":"2010-10-27T17:56:09","guid":{"rendered":"http:\/\/hgpu.org\/?p=945"},"modified":"2010-10-27T17:56:09","modified_gmt":"2010-10-27T17:56:09","slug":"cache-and-bandwidth-aware-matrix-multiplication-on-the-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=945","title":{"rendered":"Cache and bandwidth aware matrix multiplication on the GPU"},"content":{"rendered":"<p>Recent advances in the speed and programmability of consumer level graphics hardware has sparked a flurry of research that goes beyond the realm of image synthesis and computer graphics. We examine the use of the GPU (graphics processing unit) as a tool for scientific computing, by analyzing techniques for performing large matrix multiplies in GPU hardware. An earlier method for multiplying matrices on the GPU su#ered from problems of memory bandwidth. This paper examines more e#cient&#8230;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Recent advances in the speed and programmability of consumer level graphics hardware has sparked a flurry of research that goes beyond the realm of image synthesis and computer graphics. We examine the use of the GPU (graphics processing unit) as a tool for scientific computing, by analyzing techniques for performing large matrix multiplies in GPU [&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":[36,11,3],"tags":[1787,1782,37,67],"class_list":["post-945","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-paper","tag-algorithms","tag-computer-science","tag-linear-algebra","tag-performance"],"views":2179,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/945","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=945"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/945\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=945"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=945"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=945"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}