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19 July 2010 A high efficient and fast kNN algorithm based on CUDA
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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.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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);

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