{"id":7911,"date":"2012-07-14T18:32:05","date_gmt":"2012-07-14T15:32:05","guid":{"rendered":"http:\/\/hgpu.org\/?p=7911"},"modified":"2012-07-14T18:32:05","modified_gmt":"2012-07-14T15:32:05","slug":"implementing-the-approximate-message-passing-amp-algorithm-on-a-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7911","title":{"rendered":"Implementing the Approximate Message Passing (AMP) Algorithm on a GPU"},"content":{"rendered":"<p>We consider the recovery of sparse signals from a limited number of noisy observations using the AMP algorithm. In this paper, we present two fast implementations of this algorithm that run on a CPU and on a GPU and which can either be used for arbitrary unstructured measurement matrices or take advantage of the structure of a DCT matrix to give an even faster implementation. Our results show that for small problem sizes, the CPU based implementation is the fastest, but for large problem sizes, a GPU based implementation has the highest throughput.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We consider the recovery of sparse signals from a limited number of noisy observations using the AMP algorithm. In this paper, we present two fast implementations of this algorithm that run on a CPU and on a GPU and which can either be used for arbitrary unstructured measurement matrices or take advantage of the structure [&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":[36,89,33,3],"tags":[1787,14,1786,20,1089],"class_list":["post-7911","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-image-processing","category-paper","tag-algorithms","tag-cuda","tag-image-processing","tag-nvidia","tag-nvidia-geforce-gtx-560-ti"],"views":3327,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7911","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=7911"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7911\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7911"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7911"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7911"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}