{"id":10362,"date":"2013-08-21T23:44:19","date_gmt":"2013-08-21T20:44:19","guid":{"rendered":"http:\/\/hgpu.org\/?p=10362"},"modified":"2013-08-21T23:50:15","modified_gmt":"2013-08-21T20:50:15","slug":"parallel-voronoi-diagram-computation-on-scaled-distance-planes-using-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=10362","title":{"rendered":"Parallel Voronoi Diagram computation on scaled distance planes using CUDA"},"content":{"rendered":"<p>Voronoi diagrams are fundamental data structures in computational geometry with several applications on different fields inside and outside computer science. This paper shows a CUDA algorithm to compute Voronoi diagrams on a 2D image where the distance between points cannot be directly computed in the euclidean plane. The proposed method extends an existing Dijkstra-based GPU algorithm to treat our 2D images as graph and then compute the shortest-paths to create each Voronoi cell. Experimental results report speed-ups up to almost 40x over current reference sequential method for Voronoi computation on non-euclidean space. This problem is a building block of the deformation engine in the SOFA physics simulation framework.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Voronoi diagrams are fundamental data structures in computational geometry with several applications on different fields inside and outside computer science. This paper shows a CUDA algorithm to compute Voronoi diagrams on a 2D image where the distance between points cannot be directly computed in the euclidean plane. The proposed method extends an existing Dijkstra-based GPU [&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":[36,11,89,3],"tags":[1787,1782,14,20,379,133],"class_list":["post-10362","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-480","tag-voronoi-diagram"],"views":3283,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10362","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=10362"}],"version-history":[{"count":1,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10362\/revisions"}],"predecessor-version":[{"id":10367,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10362\/revisions\/10367"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10362"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10362"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10362"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}