Modern positioning and locating technologies, e.g., GPS, have generated huge amounts of geo-referenced point data that are crucial to understand environmental and social-economic phenomena. Unfortunately, traditional disk-resident databases are inefficient in handling large-scale point data. In this study, we propose to utilize the massive data parallel processing power of General Purpose computing on Graphics Processing Units (GPGPUs) technologies to index large-scale geo-referenced point data by using parallel primitives for efficiency, simplicity and portability. We have developed a CSPT-P (Constrained Space Partitioning tree for Point data) tree indexing structure that is suitable for parallel construction. Experiment results using a New York City (NYC) taxi trip dataset with nearly 170 million taxi pickup locations have demonstrated a 23X speedup on an Nvidia Quadro 6000 device over a serial CPU implementation on an Intel XEON E5405 processor.<\/p>\n","protected":false},"excerpt":{"rendered":"
Modern positioning and locating technologies, e.g., GPS, have generated huge amounts of geo-referenced point data that are crucial to understand environmental and social-economic phenomena. Unfortunately, traditional disk-resident databases are inefficient in handling large-scale point data. In this study, we propose to utilize the massive data parallel processing power of General Purpose computing on Graphics Processing […]<\/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":[89,303,3],"tags":[14,1801,20,1182],"class_list":["post-7433","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-earth-and-space-sciences","category-paper","tag-cuda","tag-earth-and-space-sciences","tag-nvidia","tag-nvidia-quadro-fx-6000"],"views":2242,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7433","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=7433"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7433\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7433"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7433"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7433"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}