Abstract
In the paper a KD-tree based filtering algorithm for K-means clustering is considered. A parallel version of the algorithm for shared memory systems, which uses OpenMP tasks both for KD-tree construction and filtering in the assignment step of K-means, is proposed. In our approach, an OpenMP task is created for a recursive call performed by tree construction and filtering procedures. A data partitioning step during the tree construction is also parallelized by OpenMP tasks. In computational experiments we measured runtimes of the parallel and serial version of the filtering algorithm and a parallel version of classical Lloyd’s algorithm for six datasets sampled from two distributions. The results of experiments, performed on a 24-core system indicate that our version filtering algorithm has very good parallel efficiency. Its runtime is up to four orders of magnitude shorter than the runtime of parallel Lloyd’s algorithm.
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Acknowledgments
This work was supported by Białystok University of Technology grant S/WI/2/2018 funded by Polish Ministry of Science and Higher Education. The calculations were carried out at the Academic Computer Centre in Gdańsk, Poland.
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Kwedlo, W., Łubowicz, M. (2020). An OpenMP Parallelization of the K-means Algorithm Accelerated Using KD-trees. In: Wyrzykowski, R., Deelman, E., Dongarra, J., Karczewski, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2019. Lecture Notes in Computer Science(), vol 12043. Springer, Cham. https://doi.org/10.1007/978-3-030-43229-4_39
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