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      K-means Clustering Algorithm: Efficient Implementation on Graphics Processing Units

      Preprint
      In review
      research-article
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      ScienceOpen Preprints
      ScienceOpen
      K-means, GPU, High Performance Computing
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            Author Summary

            Summary

            Design and implementation of Kmeans clustering algorithm on widely available graphics processing units (GPUs). Also presented is an analysis of the scalability of our proposed methods with increase in number and dimensionality of data points as well as the number of clusters and comparison of our results with current best available implementations on GPUs and a 24-way threaded parallel CPU implementation.

            Abstract

            Data analysis and classification play a big role in understanding various real life phenomena. Clustering helps analyze data with little or no prior knowledge about it. K-means clustering is a popular clustering algorithm with applications to computer vision, data mining, data visualization, etc.. Due to continuously increasing data volume, parallel computing is necessary to overcome the computational challenges involved in K-means clustering. We present the design and implementation of Kmeans clustering algorithm on widely available graphics processing units (GPUs), which have the required hardware architecture to meet these parallelism needs. We analyze the scalability of our proposed methods with increase in number and dimensionality of data points as well as the number of clusters. We also compare our results with current best available implementations on GPUs and a 24-way threaded parallel CPU implementation. We achieved a consistent speedup of 6.5x over the parallel CPU implementation.

            Content

            Author and article information

            Journal
            ScienceOpen Preprints
            ScienceOpen
            9 August 2019
            Affiliations
            [1 ] Indian Institute of Technology Kanpur
            Article
            10.14293/S2199-1006.1.SOR-.PPLHU62.v1
            54485428-f728-4445-8d42-fc45048dc3bb

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .


            All data generated or analysed during this study are included in this published article (and its supplementary information files).
            Performance, Systems & Control
            High Performance Computing,K-means,GPU

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