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

      Preprint

        , 1 , 1

      ScienceOpen Preprints

      ScienceOpen

      K-means, GPU, High Performance Computing

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          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.

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          Most cited references 4

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          K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality

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            Speeding up K-Means Algorithm by GPUs

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              Hierarchical parallel processing of large scale data clustering on a PC cluster with GPU co-processing

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                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

                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

                GPU, K-means, High Performance Computing

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