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Proceedings Paper

A high efficient and fast kNN algorithm based on CUDA
Author(s): Tong Pei; Yanxia Zhang; Yongheng Zhao
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Paper 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.

Paper Details

Date Published: 19 July 2010
PDF: 7 pages
Proc. SPIE 7740, Software and Cyberinfrastructure for Astronomy, 77402G (19 July 2010); doi: 10.1117/12.856768
Show Author Affiliations
Tong Pei, National Astronomical Observatories (China)
Yanxia Zhang, National Astronomical Observatories (China)
Yongheng Zhao, National Astronomical Observatories (China)


Published in SPIE Proceedings Vol. 7740:
Software and Cyberinfrastructure for Astronomy
Nicole M. Radziwill; Alan Bridger, Editor(s)

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