{"id":17205,"date":"2017-04-30T22:45:53","date_gmt":"2017-04-30T19:45:53","guid":{"rendered":"https:\/\/hgpu.org\/?p=17205"},"modified":"2017-04-30T22:45:53","modified_gmt":"2017-04-30T19:45:53","slug":"low-complexity-distributed-tomographic-backprojection-for-large-datasets","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=17205","title":{"rendered":"Low-complexity Distributed Tomographic Backprojection for large datasets"},"content":{"rendered":"<p>In this manuscript we present a fast GPU implementation for tomographic reconstruction of large datasets using data obtained at the Brazilian synchrotron light source. The algorithm is distributed in a cluster with 4 GPUs through a fast pipeline implemented in C programming language. Our algorithm is theoretically based on a recently discovered low complexity formula, computing the total volume within O(N3logN) floating point operations; much less than traditional algorithms that operates with O(N4) flops over an input data of size O(N3). The results obtained with real data indicate that a reconstruction can be achieved within 1 second provided the data is transferred completely to the memory.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this manuscript we present a fast GPU implementation for tomographic reconstruction of large datasets using data obtained at the Brazilian synchrotron light source. The algorithm is distributed in a cluster with 4 GPUs through a fast pipeline implemented in C programming language. Our algorithm is theoretically based on a recently discovered low complexity formula, [&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":[36,11,89,3],"tags":[1787,1782,14,20,1530,1947,1884,176,567],"class_list":["post-17205","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-gt-740-m","tag-nvidia-geforce-gtx-x","tag-nvidia-tegra-tx1","tag-package","tag-tomography"],"views":2563,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/17205","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=17205"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/17205\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=17205"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=17205"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=17205"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}