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Scalable Algorithm for Subsequence Similarity Search in Very Large Time Series Data on Cluster of Phi KNL

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Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1003))

Abstract

Nowadays, subsequence similarity search under the Dynamic Time Warping (DTW) similarity measure is applied in a wide range of time series mining applications. Since the DTW measure has a quadratic computational complexity w.r.t. the length of query subsequence, a number of parallel algorithms for various many-core architectures have been developed, namely FPGA, GPU, and Intel MIC. In this paper, we propose a novel parallel algorithm for subsequence similarity search in very large time series data on computing cluster with nodes based on the Intel Xeon Phi Knights Landing (KNL) many-core processors. Computations are parallelized both at the level of all cluster nodes through MPI, and within a single cluster node through OpenMP. The algorithm involves additional data structures and redundant computations, which make it possible to effectively use Phi KNL for vector computations. Experimental evaluation of the algorithm on real-world and synthetic datasets shows that it is highly scalable.

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Acknowledgments

This work was financially supported by the Russian Foundation for Basic Research (grant No. 17-07-00463), by Act 211 Government of the Russian Federation (contract No. 02.A03.21.0011) and by the Ministry of education and science of Russian Federation (government order 2.7905.2017/8.9). Authors thank The Siberian Branch of the Russian Academy of Sciences (SB RAS) Siberian Supercomputer Center (Novosibirsk, Russia) for the provided computational resources.

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Authors and Affiliations

  1. South Ural State University, Chelyabinsk, Russia

    Yana Kraeva & Mikhail Zymbler

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  1. Yana Kraeva
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  2. Mikhail Zymbler
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Correspondence to Mikhail Zymbler .

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Editors and Affiliations

  1. Open University of Cyprus, Latsia, Cyprus

    Yannis Manolopoulos

  2. Federal Research Center “Computer Science and Control” of RAS, Moscow, Russia

    Sergey Stupnikov

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Kraeva, Y., Zymbler, M. (2019). Scalable Algorithm for Subsequence Similarity Search in Very Large Time Series Data on Cluster of Phi KNL. In: Manolopoulos, Y., Stupnikov, S. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2018. Communications in Computer and Information Science, vol 1003. Springer, Cham. https://doi.org/10.1007/978-3-030-23584-0_9

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  • DOI: https://doi.org/10.1007/978-3-030-23584-0_9

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