{"id":2951,"date":"2011-02-24T22:12:04","date_gmt":"2011-02-24T22:12:04","guid":{"rendered":"http:\/\/hgpu.org\/?p=2951"},"modified":"2011-02-24T22:12:04","modified_gmt":"2011-02-24T22:12:04","slug":"dense-matrix-algebra-on-the-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2951","title":{"rendered":"Dense Matrix Algebra on the GPU"},"content":{"rendered":"<p>Perhaps the most important innovation of the latest generation of programmable graphics processors (GPUs) is their capability to work with floating point color data. Previous generations of GPUs have worked with up to a byte of integer data per color channel. Developers working on graphics engines with advanced lighting effects often complained about banding artifacts, even in truecolor video modes, because multiplicative effects quickly made the round off error caused by the limited precision noticeable. The advent of GPUs that represent each color channel with a 32-bit floating-point value has thus been widely celebrated in the real time graphics community.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Perhaps the most important innovation of the latest generation of programmable graphics processors (GPUs) is their capability to work with floating point color data. Previous generations of GPUs have worked with up to a byte of integer data per color channel. Developers working on graphics engines with advanced lighting effects often complained about banding artifacts, [&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":[7,996,184,1782,480,37,324],"class_list":["post-2951","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-ati","tag-ati-radeon-9500-pro","tag-ati-radeon-9700-pro","tag-computer-science","tag-directx","tag-linear-algebra","tag-matrix-multiplication"],"views":2824,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2951","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=2951"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2951\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2951"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2951"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2951"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}