{"id":13790,"date":"2015-03-28T23:09:55","date_gmt":"2015-03-28T21:09:55","guid":{"rendered":"http:\/\/hgpu.org\/?p=13790"},"modified":"2015-03-28T23:09:55","modified_gmt":"2015-03-28T21:09:55","slug":"parallel-unsteady-flow-line-integral-convolution-for-high-performance-dense-visualization","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=13790","title":{"rendered":"Parallel Unsteady Flow Line Integral Convolution for High-Performance Dense Visualization"},"content":{"rendered":"<p>This paper presents an accurate parallel implementation of unsteady flow line integral convolution (UFLIC) for high-performance visualization of large time-varying flows. Our approach differs from previous implementations by using a novel value scattering+gathering mechanism to parallelize UFLIC and designing a pathline reuse strategy to reduce the computational cost of pathline integration. By exploiting the massive parallelism of modern graphical processing units (GPU), the proposed method allows for real-time dense visualization of unsteady flows with high spatial-temporal coherence.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents an accurate parallel implementation of unsteady flow line integral convolution (UFLIC) for high-performance visualization of large time-varying flows. Our approach differs from previous implementations by using a novel value scattering+gathering mechanism to parallelize UFLIC and designing a pathline reuse strategy to reduce the computational cost of pathline integration. By exploiting the massive [&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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[89,104,3],"tags":[14,1795,333,20,1092,134],"class_list":["post-13790","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-fluid-dynamics","category-paper","tag-cuda","tag-fluid-dynamics","tag-image-generation","tag-nvidia","tag-nvidia-geforce-gtx-590","tag-visualization"],"views":3217,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13790","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=13790"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13790\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=13790"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=13790"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=13790"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}