{"id":3287,"date":"2011-03-21T11:05:18","date_gmt":"2011-03-21T11:05:18","guid":{"rendered":"http:\/\/hgpu.org\/?p=3287"},"modified":"2011-04-08T12:52:34","modified_gmt":"2011-04-08T12:52:34","slug":"gpu-based-real-time-execution-of-vehicular-mobility-models-in-large-scale-road-network-scenarios","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3287","title":{"rendered":"GPU-based Real-Time Execution of Vehicular Mobility Models in Large-Scale Road Network Scenarios"},"content":{"rendered":"<p>A methodology and its associated algorithms are presented for mapping a novel, field-based vehicular mobility model onto graphical processing unit computational platform for simulating mobility in large-scale road networks. Of particular focus is the achievement of real-time execution, on desktop platforms, of vehicular mobility on road networks comprised of millions of nodes and links, and multi-million counts of simultaneously active vehicles. The methodology is realized in a system called GARFIELD, whose implementation details and performance study are described. The runtime characteristics of a prototype implementation are presented that show real-time performance in simulations of networks at the scale of a few states of the US road networks.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A methodology and its associated algorithms are presented for mapping a novel, field-based vehicular mobility model onto graphical processing unit computational platform for simulating mobility in large-scale road networks. Of particular focus is the achievement of real-time execution, on desktop platforms, of vehicular mobility on road networks comprised of millions of nodes and links, 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":[11,3],"tags":[444,514,163,1782,20,183,182],"class_list":["post-3287","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-cg","tag-computational-engineering","tag-computational-modelling","tag-computer-science","tag-nvidia","tag-nvidia-geforce-8800-gtx","tag-opengl"],"views":2111,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3287","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=3287"}],"version-history":[{"count":1,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3287\/revisions"}],"predecessor-version":[{"id":3517,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3287\/revisions\/3517"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3287"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3287"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3287"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}