A high efficient and fast kNN algorithm based on CUDA

Tong Pei; Yanxia Zhang; Yongheng Zhao

doi:10.1117/12.856768

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

Tong Pei, Yanxia Zhang, Yongheng Zhao

Author Affiliations +

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE