A high efficient and fast kNN algorithm based on CUDA

Details A high efficient and fast kNN algorithm based on CUDA A high efficient and fast kNN algorithm based on CUDA

Jul 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010spie.7740e..81p&link_type=abstract

Software and Cyberinfrastructure for Astronomy. Edited by Radziwill, Nicole M.; Bridger, Alan. Proceedings of the SPIE, Volume 7

Astronomy and Astrophysics

Astronomy

3

Scientific paper

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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