{"id":7008,"date":"2012-01-23T13:12:01","date_gmt":"2012-01-23T11:12:01","guid":{"rendered":"http:\/\/hgpu.org\/?p=7008"},"modified":"2012-01-23T13:12:01","modified_gmt":"2012-01-23T11:12:01","slug":"cuda-based-polyphase-filter","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7008","title":{"rendered":"CUDA Based Polyphase Filter"},"content":{"rendered":"<p>This paper presents the evaluation of the use of a graphics processor for realtime radio astronomy DSP (Digital Signal Processing) within VLBI (Very Long Baseline Interferometry). A polyphase filter bank (pfb) was implemented in a prototype application to convert external ADC input into channelized frequency streams. This system was tested with a 32 channel pfb, 8 bit samples, and 8 taps\/channel. With a prototype system, 512 Mega-samples\/second could be easily processed and 890 Mega-samples\/second is possible. Instruction throughput is the current limitation, so a modest increase in the graphics card&#8217;s processing speed will permit the desired speed of 1024 Megasamples\/second. This makes GPUs an interesting candidate for a cost effective upgrade as both software and hardware systems progress.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents the evaluation of the use of a graphics processor for realtime radio astronomy DSP (Digital Signal Processing) within VLBI (Very Long Baseline Interferometry). A polyphase filter bank (pfb) was implemented in a prototype application to convert external ADC input into channelized frequency streams. This system was tested with a 32 channel pfb, [&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":[89,3,41],"tags":[14,809,20,176,1789,378],"class_list":["post-7008","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-paper","category-signal-processing","tag-cuda","tag-dsp","tag-nvidia","tag-package","tag-signal-processing","tag-tesla-c2050"],"views":3096,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7008","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=7008"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7008\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7008"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7008"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7008"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}