{"id":6867,"date":"2012-01-08T16:55:37","date_gmt":"2012-01-08T14:55:37","guid":{"rendered":"http:\/\/hgpu.org\/?p=6867"},"modified":"2012-01-08T16:55:37","modified_gmt":"2012-01-08T14:55:37","slug":"a-quasi-parallel-gpu-based-algorithm-for-delaunay-edge-flips","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6867","title":{"rendered":"A Quasi-Parallel GPU-Based Algorithm for Delaunay Edge-Flips"},"content":{"rendered":"<p>The Delaunay edge-flip algorithm is a practical method for transforming any existing triangular mesh S into a mesh T(S) that satisfies the Delaunay condition. Although several implementations of this algorithm are known, to the best of our knowledge no parallel GPU-based implementation has been reported yet. In the present work, we propose a quadriphasic and iterative GPU-based algorithm that transforms 2D triangulations and 3D triangular surface meshes into Delaunay triangulations and improves strongly the performance with respect to a sequential CPU-implementation in large meshes. For 3D surface triangulations, we use a threshold value to prevent edge-flips in triangles that could deform the original geometry. On each phase, we address the main challenges that arise when adapting the problem to a parallel architecture and we present a GPU-based solution for each high CPU-consuming time step, reducing drastically the number sequential operations needed.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Delaunay edge-flip algorithm is a practical method for transforming any existing triangular mesh S into a mesh T(S) that satisfies the Delaunay condition. Although several implementations of this algorithm are known, to the best of our knowledge no parallel GPU-based implementation has been reported yet. In the present work, we propose a quadriphasic and [&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":[36,11,89,3],"tags":[1787,1782,14,20,411,182,138],"class_list":["post-6867","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-9800-gtx","tag-opengl","tag-triangular-meshes"],"views":2293,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6867","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=6867"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6867\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6867"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6867"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6867"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}