Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/67733
Title: Accelerating exact similarity search on CPU-GPU systems
Authors: Matsumoto, T
Yiu, ML 
Keywords: Heapsort
GPU
Parallel
Heterogeneous processing
Data mining
Similarity search
Nearest neighbor
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE International Conference on Data Mining (ICDM), Atlantic City, NJ, November 14-17, 2015, p.320-329 How to cite?
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.
URI: http://hdl.handle.net/10397/67733
ISBN: 978-1-4673-9504-5 (electronic)
978-1-4673-9503-8 (CD-ROM)
ISSN: 1550-4786
DOI: 10.1109/ICDM.2015.125
Appears in Collections:Conference Paper

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