{"id":10505,"date":"2013-09-11T01:08:31","date_gmt":"2013-09-10T22:08:31","guid":{"rendered":"http:\/\/hgpu.org\/?p=10505"},"modified":"2013-09-11T01:08:31","modified_gmt":"2013-09-10T22:08:31","slug":"a-mixed-hierarchical-algorithm-for-nearest-neighbor-search","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=10505","title":{"rendered":"A Mixed Hierarchical Algorithm for Nearest Neighbor Search"},"content":{"rendered":"<p>The k nearest neighbor (kNN) search is a computationally intensive application critical to fields such as image processing, statistics, and biology. Recent works have demonstrated the efficacy of k-d tree based implementations on multi-core CPUs. It is unclear, however, whether such tree based implementations are amenable for execution in high-density processors typified today by the graphics processing unit (GPU). This work seeks to map and optimize kNN to massively parallel architectures such as the GPU. Our approach synthesizes a clustering technique, k-means, with traditional brute force methods to prune the search space while taking advantage of data-parallel execution of kNN on the GPU.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The k nearest neighbor (kNN) search is a computationally intensive application critical to fields such as image processing, statistics, and biology. Recent works have demonstrated the efficacy of k-d tree based implementations on multi-core CPUs. It is unclear, however, whether such tree based implementations are amenable for execution in high-density processors typified today by the [&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":[1787,1782,14,349,20,1226],"class_list":["post-10505","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-nearest-neighbour","tag-nvidia","tag-tesla-c2075"],"views":2690,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10505","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=10505"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10505\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10505"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10505"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10505"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}