A GPU-accelerated parallel K-means algorithm. (May 2019)

• Record Type: Journal Article
• Authors: Cuomo, S.; De Angelis, V.; Farina, G.; Marcellino, L.; Toraldo, G.
• Is Part Of: Computers & electrical engineering. Volume 75(2019)
• Page Start: 262
• Page End: 274
• Publication Date: 2019-05
• Subjects: Clustering -- K-means -- Graphic Processor Units -- Parallel processing; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.12.002
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: 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.