NDLI: Accelerating Exact Similarity Search on CPU-GPU Systems

Content Provider	IEEE Xplore Digital Library
Author	Matsumoto, T. Man Lung Yiu
Copyright Year	2015
Description	Author affiliation: Dept. of Comput., Hong Kong Polytech. Univ., Hong Kong, China (Matsumoto, T.; Man Lung Yiu)
Abstract	In recent years, the use of Graphics Processing Units (GPUs) for data mining tasks has become popular. With modern processors integrating both CPUs and GPUs, it is also important to consider what tasks benefit from GPU processing and which do not, and apply a heterogeneous processing approach to improve the efficiency where applicable. Similarity search, also known as k-nearest neighbor search, is a key part of data mining applications and is used also extensively in applications such as multimedia search, where only a small subset of possible results are used. Our contribution is a new exact kNN algorithm with a compressed partial heapsort that outperforms other state-of-the-art exact kNN algorithms by leveraging both the GPU and CPU.
Starting Page	320
Ending Page	329
File Size	349843
Page Count	10
File Format	PDF
ISSN	15504786
e-ISBN	9781467395045
DOI	10.1109/ICDM.2015.125
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Graphics processing units Sorting Instruction sets Force Data mining Approximation methods heapsort GPU parallel heterogeneous processing data mining similarity search nearest neighbor
Content Type	Text
Resource Type	Article

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