A high efficient and fast kNN algorithm based on CUDA

Tong Pei; Yanxia Zhang; Yongheng Zhao

doi:10.1117/12.856768

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19 July 2010 A high efficient and fast kNN algorithm based on CUDA

Tong Pei, Yanxia Zhang, Yongheng Zhao

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Proceedings Volume 7740, Software and Cyberinfrastructure for Astronomy; 77402G (2010) https://doi.org/10.1117/12.856768
Event: SPIE Astronomical Telescopes + Instrumentation, 2010, San Diego, California, United States

Abstract

The k Nearest Neighbor (kNN) algorithm is an effective classification approach in the statistical methods of pattern recognition. But it could be a rather time-consuming approach when applied on massive data, especially facing large survey projects in astronomy. NVIDIA CUDA is a general purpose parallel computing architecture that leverages the parallel compute engine in NVIDIA graphics processing units (GPUs) to solve many complex computational problems in a fraction of the time required on a CPU. In this paper, we implement a CUDAbased kNN algorithm, and compare its performance with CPU-only kNN algorithm using single-precision and double-precision datatype on classifying celestial objects. The results demonstrate that CUDA can speedup kNN algorithm effectively and could be useful in astronomical applications.

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Tong Pei, Yanxia Zhang, and Yongheng Zhao "A high efficient and fast kNN algorithm based on CUDA", Proc. SPIE 7740, Software and Cyberinfrastructure for Astronomy, 77402G (19 July 2010); https://doi.org/10.1117/12.856768

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