{"id":15920,"date":"2016-05-26T01:05:24","date_gmt":"2016-05-25T22:05:24","guid":{"rendered":"http:\/\/hgpu.org\/?p=15920"},"modified":"2016-05-26T01:21:37","modified_gmt":"2016-05-25T22:21:37","slug":"faster-gpu-based-convolutional-gridding-via-thread-coarsening","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=15920","title":{"rendered":"Faster GPU-based convolutional gridding via thread coarsening"},"content":{"rendered":"<p>Convolutional gridding is a processor-intensive step in interferometric imaging. While it is possible to use graphics processing units (GPUs) to accelerate this operation, existing methods use only a fraction of the available flops. We apply thread coarsening to improve the efficiency of an existing algorithm, and observe performance gains of up to 3.2x for single-polarization gridding and 1.9x for quad-polarization gridding on a GeForce GTX 980, and smaller but still significant gains on a Radeon R9 290X.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Convolutional gridding is a processor-intensive step in interferometric imaging. While it is possible to use graphics processing units (GPUs) to accelerate this operation, existing methods use only a fraction of the available flops. We apply thread coarsening to improve the efficiency of an existing algorithm, and observe performance gains of up to 3.2x for single-polarization [&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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,96,11,90,3],"tags":[1787,1719,1794,7,1782,97,20,1650,1793],"class_list":["post-15920","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-astrophysics","category-computer-science","category-opencl","category-paper","tag-algorithms","tag-amd-radeon-r9-290x","tag-astrophysics","tag-ati","tag-computer-science","tag-instrumentation-and-methods-for-astrophysics","tag-nvidia","tag-nvidia-geforce-gtx-980","tag-opencl"],"views":2290,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/15920","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=15920"}],"version-history":[{"count":1,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/15920\/revisions"}],"predecessor-version":[{"id":15921,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/15920\/revisions\/15921"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=15920"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=15920"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=15920"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}