{"id":8909,"date":"2013-02-09T23:00:56","date_gmt":"2013-02-09T21:00:56","guid":{"rendered":"http:\/\/hgpu.org\/?p=8909"},"modified":"2013-02-09T23:00:56","modified_gmt":"2013-02-09T21:00:56","slug":"distributed-multi-node-multi-gpu-heterogeneous-system-for-3d-image-reconstruction-in-electrical-capacitance-tomography-network-performance-and-application-analysis","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8909","title":{"rendered":"Distributed multi-node, multi-GPU, heterogeneous system for 3D image reconstruction in Electrical Capacitance Tomography &#8211; network performance and application analysis"},"content":{"rendered":"<p>3D ECT provides a lot of challenging computational issues as image reconstruction requires execution of many basic operations of linear algebra, especially when the solutions are based on Finite Element Method. In order to reach real-time reconstruction a 3D ECT computational subsystem has to be able to transform capacitance data into image in fractions of seconds. By performing computations in parallel and in a distributed, heterogeneous, multi-GPU environment a significant speed-up can be achieved. Nevertheless performed tests clearly illustrate the need for developing a highly optimized distributed platform, which would mitigate existing hardware and software limitations.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>3D ECT provides a lot of challenging computational issues as image reconstruction requires execution of many basic operations of linear algebra, especially when the solutions are based on Finite Element Method. In order to reach real-time reconstruction a 3D ECT computational subsystem has to be able to transform capacitance data into image in fractions of [&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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[33,38,3],"tags":[7,645,1037,212,452,1786,512,37,1788,20,1006,244,567],"class_list":["post-8909","post","type-post","status-publish","format-standard","hentry","category-image-processing","category-medicine","category-paper","tag-ati","tag-ati-radeon-hd-5970","tag-fem","tag-finite-element-method","tag-heterogeneous-systems","tag-image-processing","tag-image-reconstruction","tag-linear-algebra","tag-medicine","tag-nvidia","tag-tesla-c2070","tag-tesla-s1070","tag-tomography"],"views":3427,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8909","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=8909"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8909\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8909"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8909"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8909"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}