{"id":3716,"date":"2011-04-26T09:16:49","date_gmt":"2011-04-26T09:16:49","guid":{"rendered":"http:\/\/hgpu.org\/?p=3716"},"modified":"2011-04-26T09:16:49","modified_gmt":"2011-04-26T09:16:49","slug":"making-human-connectome-faster-gpu-acceleration-of-brain-network-analysis","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3716","title":{"rendered":"Making Human Connectome Faster: GPU Acceleration of Brain Network Analysis"},"content":{"rendered":"<p>The research on complex Brain Networks plays a vital role in understanding the connectivity patterns of the human brain and disease-related alterations. Recent studies have suggested a noninvasive way to model and analyze human brain networks by using multi-modal imaging and graph theoretical approaches. Both the construction and analysis of the Brain Networks require tremendous computation. As a result, most current studies of the Brain Networks are focused on a coarse scale based on Brain Regions. Networks on this scale usually consist around 100 nodes. The more accurate and meticulous voxel-base Brain Networks, on the other hand, may consist 20K to 100K nodes. In response to the difficulties of analyzing large-scale networks, we propose an acceleration framework for voxel-base Brain Network Analysis based on Graphics Processing Unit (GPU). Our GPU implementations of Brain Network construction and modularity achieve 24x and 80x speedup respectively, compared with single-core CPU. Our work makes the processing time affordable to analyze multiple large-scale Brain Networks.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The research on complex Brain Networks plays a vital role in understanding the connectivity patterns of the human brain and disease-related alterations. Recent studies have suggested a noninvasive way to model and analyze human brain networks by using multi-modal imaging and graph theoretical approaches. Both the construction and analysis of the Brain Networks require tremendous [&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":[88,38,3],"tags":[7,84,455,1792,1788,632,506],"class_list":["post-3716","post","type-post","status-publish","format-standard","hentry","category-ati-stream","category-medicine","category-paper","tag-ati","tag-ati-il","tag-ati-radeon-hd-5870","tag-ati-stream","tag-medicine","tag-neurons-and-cognition","tag-neuroscience"],"views":3935,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3716","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=3716"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3716\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3716"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3716"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3716"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}