{"id":13078,"date":"2014-11-16T19:32:11","date_gmt":"2014-11-16T17:32:11","guid":{"rendered":"http:\/\/hgpu.org\/?p=13078"},"modified":"2014-11-16T19:32:11","modified_gmt":"2014-11-16T17:32:11","slug":"the-implementation-of-a-real-time-polyphase-filter","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=13078","title":{"rendered":"The Implementation of a Real-Time Polyphase Filter"},"content":{"rendered":"<p>In this article we study the suitability of different computational accelerators for the task of real-time data processing. The algorithm used for comparison is the polyphase filter, a standard tool in signal processing and a well established algorithm. We measure performance in FLOPs and execution time, which is a critical factor for real-time systems. For our real-time studies we have chosen a data rate of 6.5GB\/s, which is the estimated data rate for a single channel on the SKAs Low Frequency Aperture Array. Our findings how that GPUs are the most likely candidate for real-time data processing. GPUs are better in both performance and power consumption.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this article we study the suitability of different computational accelerators for the task of real-time data processing. The algorithm used for comparison is the polyphase filter, a standard tool in signal processing and a well established algorithm. We measure performance in FLOPs and execution time, which is a critical factor for real-time systems. For [&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":[89,3,41],"tags":[14,1483,20,974,1634,67,1789,1543],"class_list":["post-13078","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-paper","category-signal-processing","tag-cuda","tag-intel-xeon-phi","tag-nvidia","tag-nvidia-geforce-gtx-580","tag-nvidia-geforce-gtx-750-ti","tag-performance","tag-signal-processing","tag-tesla-k40"],"views":2285,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13078","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=13078"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13078\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=13078"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=13078"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=13078"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}