The k-nearest neighbours (k-NN) search is one of the most critical nonparametric methods used in data retrieval and similarity tasks. Over recent years fast k-NN processing for large amount of high-dimensional data is increasingly demanded. Locality-sensitive hashing is a viable solution for computing fast approximate nearest neighbours (ANN) with reasonable accuracy. This chapter presents a novel parallelization of the locality-sensitive hashing method using GPGPU, where the multi-probe variant is considered. The method was implemented using CUDA platform for constructing a k-ANN graph. It was compared to the state-of-the-art CPU-based k-ANN and two GPU-based k-NN methods on large and multidimensional dataset. The experimental results showed that the proposed method has a speedup of 30x or higher, in comparison to the CPU-based approximate method, whilst retaining a high recall rate.<\/p>\n","protected":false},"excerpt":{"rendered":"
The k-nearest neighbours (k-NN) search is one of the most critical nonparametric methods used in data retrieval and similarity tasks. Over recent years fast k-NN processing for large amount of high-dimensional data is increasingly demanded. Locality-sensitive hashing is a viable solution for computing fast approximate nearest neighbours (ANN) with reasonable accuracy. This chapter presents a […]<\/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":[11,89,3],"tags":[1782,14,132,349,20,378],"class_list":["post-10948","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-hashing","tag-nearest-neighbour","tag-nvidia","tag-tesla-c2050"],"views":2938,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10948","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=10948"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10948\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10948"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10948"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10948"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}