{"id":8518,"date":"2012-11-18T23:33:14","date_gmt":"2012-11-18T21:33:14","guid":{"rendered":"http:\/\/hgpu.org\/?p=8518"},"modified":"2012-11-18T23:33:14","modified_gmt":"2012-11-18T21:33:14","slug":"using-graphics-processing-units-to-parallelize-the-fdk-algorithm-for-tomographic-image-reconstruction","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8518","title":{"rendered":"Using Graphics Processing Units to Parallelize the FDK Algorithm for Tomographic Image Reconstruction"},"content":{"rendered":"<p>The paper presents the implementation of a parallel version of FDK (Felkamp, David e Kress) algorithm using graphics processing units. Discussion was briefly some elements the computed tomographic scan and FDK algorithm; and some ideas about GPUs (Graphics Processing Units) and its use in general purpose computing were presented. The paper shows a computational implementation of FDK algorithm and the process of parallelization of this implementation. Compare the parallel version of the algorithm with the sequential version, used speedup as a performance metric. To evaluate the performance of parallel version, two GPUs, GeForce 9400GT (16 cores) a low capacity GPU and Quadro 2000 (192 cores) a medium capacity GPU was reached speedup of 3.37.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The paper presents the implementation of a parallel version of FDK (Felkamp, David e Kress) algorithm using graphics processing units. Discussion was briefly some elements the computed tomographic scan and FDK algorithm; and some ideas about GPUs (Graphics Processing Units) and its use in general purpose computing were presented. The paper shows a computational implementation [&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":[89,38,3],"tags":[479,478,14,512,1788,20,453,1231],"class_list":["post-8518","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-medicine","category-paper","tag-computed-tomography","tag-ct","tag-cuda","tag-image-reconstruction","tag-medicine","tag-nvidia","tag-nvidia-geforce-9400-m","tag-nvidia-quadro-fx-2000"],"views":3037,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8518","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=8518"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8518\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8518"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8518"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8518"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}