{"id":1660,"date":"2010-11-26T15:13:20","date_gmt":"2010-11-26T15:13:20","guid":{"rendered":"http:\/\/hgpu.org\/?p=1660"},"modified":"2010-11-26T15:13:20","modified_gmt":"2010-11-26T15:13:20","slug":"profile-guided-optimization-of-critical-medical-imaging-algorithms","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1660","title":{"rendered":"Profile-guided optimization of critical medical imaging algorithms"},"content":{"rendered":"<p>Given the rapid growth in computational requirements for medical image analysis, Graphics Processing Units (GPUs) have begun to be utilized to address these demands. But even though GPUs are well-suited to the underlying processing associated with medical image reconstruction, extracting the full benefits of moving to GPU platforms requires significant programming effort, and presents a fundamental barrier for more general adoption of GPU acceleration in a wider range of medical imaging applications. In this paper we describe our experience in accelerating a number of challenging medical imaging applications, and discuss how we utilize profile-guided analysis to reap the full benefits available on GPU platforms. Our work considers different GPU architectures, as well as how to fully exploit the benefits of using multiple GPUs.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Given the rapid growth in computational requirements for medical image analysis, Graphics Processing Units (GPUs) have begun to be utilized to address these demands. But even though GPUs are well-suited to the underlying processing associated with medical image reconstruction, extracting the full benefits of moving to GPU platforms requires significant programming effort, and presents a [&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":[33,3],"tags":[1786,512,298,567],"class_list":["post-1660","post","type-post","status-publish","format-standard","hentry","category-image-processing","category-paper","tag-image-processing","tag-image-reconstruction","tag-optimization","tag-tomography"],"views":1899,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1660","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=1660"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1660\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1660"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1660"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1660"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}