{"id":12518,"date":"2014-07-18T23:46:43","date_gmt":"2014-07-18T20:46:43","guid":{"rendered":"http:\/\/hgpu.org\/?p=12518"},"modified":"2014-07-18T23:46:43","modified_gmt":"2014-07-18T20:46:43","slug":"suitability-of-nvidia-gpus-for-ska1-low","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=12518","title":{"rendered":"Suitability of NVIDIA GPUs for SKA1-Low"},"content":{"rendered":"<p>In this memo we investigate the applicability of NVIDIA Graphics Processing Units (GPUs) for SKA1-Low station and Central Signal Processing (CSP)-level processing. Station-level processing primarily involves generating a single station beam which will then be correlated with other beams in CSP. Fine channelisation can be performed either at the station of CSP-level, while coarse channelisation is assumed to be performed on FPGA-based Tile Processors, together with A\/D conversion, equilisation and other processes. Rough estimates for number of GPUs required and power requirements will also be provided.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this memo we investigate the applicability of NVIDIA Graphics Processing Units (GPUs) for SKA1-Low station and Central Signal Processing (CSP)-level processing. Station-level processing primarily involves generating a single station beam which will then be correlated with other beams in CSP. Fine channelisation can be performed either at the station of CSP-level, while coarse channelisation [&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":[96,89,3,41],"tags":[1794,14,377,97,20,1634,1789,1543],"class_list":["post-12518","post","type-post","status-publish","format-standard","hentry","category-astrophysics","category-nvidia-cuda","category-paper","category-signal-processing","tag-astrophysics","tag-cuda","tag-fpga","tag-instrumentation-and-methods-for-astrophysics","tag-nvidia","tag-nvidia-geforce-gtx-750-ti","tag-signal-processing","tag-tesla-k40"],"views":1918,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12518","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=12518"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12518\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12518"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12518"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12518"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}