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A GPU-accelerated parallel K-means algorithm
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DOI：10.1016/j.compeleceng.2017.12.002.png)
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Abstract
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Clustering approaches are widely used methodologies to analyse large data sets. The K-means algorithm is well-known as a procedure too computational-intensive for the large data analytic problem. In this work, we focus on a parallel technique to reduce the execution time when the K-means is used to cluster large dataset. We exploit computational powerful of its design when the Graphic Processor Units (GPUs), a massively parallel architecture, is adopted. We optimize the proposed implementation to handle (i) the space limitation issue of GPUs: (ii) the host-device data transfer time. Experimental results, on real and synthetic data, show how our parallelization approach give good results in terms of execution time and speed-up. (C) 2017 Elsevier Ltd. All rights reserved.
Keywords:
Clustering
K-means
Graphic Processor Units
Parallel processing
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