{"id":11350,"date":"2014-02-04T00:18:46","date_gmt":"2014-02-03T22:18:46","guid":{"rendered":"http:\/\/hgpu.org\/?p=11350"},"modified":"2014-02-04T00:18:46","modified_gmt":"2014-02-03T22:18:46","slug":"a-scalable-hybrid-fpgagpu-fx-correlator","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=11350","title":{"rendered":"A Scalable Hybrid FPGA\/GPU FX Correlator"},"content":{"rendered":"<p>Radio astronomical imaging arrays comprising large numbers of antennas, O(10^2-10^3) have posed a signal processing challenge because of the required O(N^2) cross correlation of signals from each antenna and requisite signal routing. This motivated the implementation of a Packetized Correlator architecture that applies Field Programmable Gate Arrays (FPGAs) to the O(N) &quot;F-stage&quot; transforming time domain to frequency domain data, and Graphics Processing Units (GPUs) to the O(N^2) &quot;X-stage&quot; performing an outer product among spectra for each antenna. The design is readily scalable to at least O(10^3) antennas. Fringes, visibility amplitudes and sky image results obtained during field testing are presented.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Radio astronomical imaging arrays comprising large numbers of antennas, O(10^2-10^3) have posed a signal processing challenge because of the required O(N^2) cross correlation of signals from each antenna and requisite signal routing. This motivated the implementation of a Packetized Correlator architecture that applies Field Programmable Gate Arrays (FPGAs) to the O(N) &quot;F-stage&quot; transforming time domain [&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":[1794,14,377,97,20,1789,378],"class_list":["post-11350","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-signal-processing","tag-tesla-c2050"],"views":2364,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11350","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=11350"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11350\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11350"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11350"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11350"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}