With large volumes of complex data generated by different applications, Machine Learning (ML) algorithms alone may not yield significant performance benefits on a single or multi-core CPU. Applying optimization techniques to these ML algorithms in a High-Performance Computing (HPC) environment can give considerable speedups for high-dimensional datasets. One of the most widely used classification algorithms, with applications in various domains, is the K-Nearest Neighbor (KNN). Despite its simplicity, KNN poses several challenges while handling high-dimensional data. However, the algorithm\u2019s inherent nature presents an opportunity for parallelization. This paper reviews the optimization techniques employed by several researchers to accelerate the KNN algorithm on a GPU platform. The study reveals that techniques such as coalesced-memory access, tiling with shared memory, chunking, data segmentation, and pivot-based partitioning significantly contribute towards speeding up the KNN algorithm to leverage the GPU capabilities. The algorithms reviewed have performed exceptionally well on high-dimensional data with speedups up to 750x for a dual-GPU platform and up to 1840x for a multi-GPU platform. This study serves as a valuable resource for researchers examining KNN acceleration in high-performance computing environments and its applications in various fields.<\/p>\n","protected":false},"excerpt":{"rendered":"
With large volumes of complex data generated by different applications, Machine Learning (ML) algorithms alone may not yield significant performance benefits on a single or multi-core CPU. Applying optimization techniques to these ML algorithms in a High-Performance Computing (HPC) environment can give considerable speedups for high-dimensional datasets. One of the most widely used classification algorithms, […]<\/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":[11,3],"tags":[1782,1025,349,20,31],"class_list":["post-30219","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-computer-science","tag-machine-learning","tag-nearest-neighbour","tag-nvidia","tag-review"],"views":1009,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/30219","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=30219"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/30219\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=30219"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=30219"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=30219"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}