{"id":7996,"date":"2012-08-01T17:26:47","date_gmt":"2012-08-01T14:26:47","guid":{"rendered":"http:\/\/hgpu.org\/?p=7996"},"modified":"2012-08-01T17:26:47","modified_gmt":"2012-08-01T14:26:47","slug":"high-level-manipulation-of-opencl-based-subvectors-and-submatrices","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7996","title":{"rendered":"High-Level Manipulation of OpenCL-Based Subvectors and Submatrices"},"content":{"rendered":"<p>High-level C++ proxies for the convenient manipulation of subvectors and submatrices on OpenCL-enabled devices are introduced. It is demonstrated that the programming convenience of standard host-based code can be retained using native C++ language features only, even if massively parallel computing architectures such as graphics processing units are employed. The required modifications of the underlying OpenCL kernels are discussed and a case study of an implementation of the QR-factorization is given. Benchmark results confirm that the convenience of purely CPU-based libraries can be preserved without sacrificing performance of OpenCL-enabled devices, particularly graphics processing units.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>High-level C++ proxies for the convenient manipulation of subvectors and submatrices on OpenCL-enabled devices are introduced. It is demonstrated that the programming convenience of standard host-based code can be retained using native C++ language features only, even if massively parallel computing architectures such as graphics processing units are employed. The required modifications of the underlying [&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":[11,90,3],"tags":[1782,288,20,953,1793],"class_list":["post-7996","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-computer-science","tag-factorization","tag-nvidia","tag-nvidia-geforce-gtx-470","tag-opencl"],"views":2480,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7996","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=7996"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7996\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7996"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7996"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7996"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}