{"id":6254,"date":"2011-11-12T17:43:12","date_gmt":"2011-11-12T15:43:12","guid":{"rendered":"http:\/\/hgpu.org\/?p=6254"},"modified":"2011-11-12T17:43:12","modified_gmt":"2011-11-12T15:43:12","slug":"a-multi-gpu-acceleration-for-3d-imaging-of-the-prostate","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6254","title":{"rendered":"A multi-GPU acceleration for 3D imaging of the prostate"},"content":{"rendered":"<p>Transrectal Electric Impedance Tomography (TREIT) has been proposed jointly with ultrasound (US) imaging of the prostate to enhance the standard clinical imaging. Reconstructing TREIT images involves a solution of an inverse problem. The reconstruction is based on two steps: solving and updating an estimate of the dielectric property distribution through solution of an inverse problem. In this paper we consider a multi-GPU acceleration, which will allow us to significantly speed up the solution of the inverse problem. By conducting numerical experiments we compare results in assembling the Jacobian matrix by a CPU-based multiple computational cores server, a single GPU acceleration, and eventually a multi-GPU.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Transrectal Electric Impedance Tomography (TREIT) has been proposed jointly with ultrasound (US) imaging of the prostate to enhance the standard clinical imaging. Reconstructing TREIT images involves a solution of an inverse problem. The reconstruction is based on two steps: solving and updating an estimate of the dielectric property distribution through solution of an inverse problem. [&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":[14,1788,20,244,567,208],"class_list":["post-6254","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-medicine","category-paper","tag-cuda","tag-medicine","tag-nvidia","tag-tesla-s1070","tag-tomography","tag-ultrasound"],"views":1854,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6254","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=6254"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6254\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6254"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6254"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6254"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}