NDLI: Design and evaluation of a parallel k-nearest neighbor algorithm on CUDA-enabled GPU

Content Provider	IEEE Xplore Digital Library
Author	Shenshen Liang Ying Liu Cheng Wang Liheng Jian
Copyright Year	2010
Description	Author affiliation: Agilent Technologies, Beijing, 100102, China (Cheng Wang) \|\| Graduate University of Chinese Academy of Sciences, Beijing, 100190, China (Shenshen Liang; Ying Liu; Liheng Jian)
Abstract	Recent developments in Graphics Processing Units (GPUs) have enabled inexpensive high performance computing for general-purpose applications. Due to GPU's tremendous computing capability, it has emerged as the co-processor of CPU to achieve a high overall throughput. CUDA programming model provides the programmers adequate C language like APIs to better exploit the parallel power of the GPU. K-nearest neighbor (KNN) is a widely used classification technique and has significant applications in various domains, especially in text classification. The computational-intensive nature of KNN requires a high performance implementation. In this paper, we propose CUKNN, a CUDA-based parallel implementation of KNN. It launches two CUDA kernels, distance calculation kernel and selecting kernel. In the distance calculation kernel, a great number of concurrent CUDA threads are issued, where each thread performs the calculation between the query object and a reference object; in the selecting kernel, threads in a block find the local-k nearest neighbors of the query object concurrently, and then a thread is invoked to find the global-k nearest neighbors out of the queues of local-k neighbors. Various CUDA optimization techniques are applied to maximize the utilization of GPU. We evaluate our implementation by using synthetic dataseis and a real physical simulation dataset. The experimental results demonstrate that CUKNN outperforms the serial KNN on an HP xw8600 workstation significantly, achieving up to 46.7IX speedup on the synthetic dataseis and 42.49X on the physical simulation dataset including I/O cost. It also shows good scalability when varying the number of dimensions of the reference dataset, the number of objects in the reference dataset, and the number of objects in the query dataset.
Starting Page	53
Ending Page	60
File Size	293681
Page Count	8
File Format	PDF
ISBN	9781424463565
e-ISBN	9781424463596
DOI	10.1109/SWS.2010.5607480
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Instruction sets Computational modeling Programming Classification algorithms Kernel Graphics processing unit Nearest neighbor searches
Content Type	Text
Resource Type	Article

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