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Discovery of Time Series Motifs on Intel Many-Core Systems

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Abstract

A motif is a pair of subsequences of a longer time series, which are very similar to each other. Motif discovery is applied in a wide range of subject areas involving time series: medicine, biology, entertainment, weather prediction, and others. In this paper, we propose a novel parallel algorithm for motif discovery using Intel MIC (Many Integrated Core) accelerators in the case when time series fit in the main memory. We perform parallelization through thread-level parallelism and OpenMP technology. The algorithm employs a set of matrix data structures to store and index the subsequences of a time series and to provide an efficient vectorization of computations on the Intel MIC platform. The experimental evaluation shows the high scalability of the proposed algorithm.

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Funding

This work was financially supported by the Russian Foundation for Basic Research (grant no. 17-07-00463) and by the Ministry of Science and Higher Education of the Russian Federation (government orders 2.7905.2017/8.9 and 14.578.21.0265).

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Authors and Affiliations

  1. South Ural State University, Chelyabinsk, 454080, Russia

    M. L. Zymbler & Ya. A. Kraeva

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  1. M. L. Zymbler
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  2. Ya. A. Kraeva
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Correspondence to M. L. Zymbler or Ya. A. Kraeva.

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Submitted by A. M. Elizarov

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Zymbler, M.L., Kraeva, Y.A. Discovery of Time Series Motifs on Intel Many-Core Systems. Lobachevskii J Math 40, 2124–2132 (2019). https://doi.org/10.1134/S199508021912014X

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  • DOI: https://doi.org/10.1134/S199508021912014X

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