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A Study of Euclidean Distance Matrix Computation on Intel Many-Core Processors

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Parallel Computational Technologies (PCT 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 910))

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Abstract

Computation of a Euclidean distance matrix (EDM) is a typical task in a wide spectrum of problems connected with data analysis. Currently, many parallel algorithms for this task have been developed for GPUs. However, these developments cannot be directly applied to the Intel Xeon Phi many-core processor. In this paper, we address the task of accelerating EDM computation on Intel Xeon Phi in the case when the input data fit into the main memory. We present a parallel algorithm based on a novel block-oriented scheme of computations that allows for the efficient utilization of Intel Xeon Phi vectorization abilities. Experimental evaluation of the algorithm on real-world and synthetic datasets shows that it is highly scalable and outruns analogues in the case of rectangular matrices with low-dimensional data points.

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Notes

  1. 1.

    Strictly speaking, an EDM should contain Euclidean distances, and not the squares thereof. However, we adhere to this ambiguous convention in order to ensure compatibility with most papers related to EDMs [5].

  2. 2.

    Note that this definition also covers the case \(\mathbf {A} \equiv \mathbf {B}\).

  3. 3.

    Intel Math Kernel Library 2018 Release Notes.

  4. 4.

    NVIDIA Tesla C2050/C2070 Data sheet.

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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 of the Government of the Russian Federation (contract No. 02.A03.21.0011) and by the Ministry of Education and Science of the Russian Federation (government order 2.7905.2017/8.9).

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Authors and Affiliations

  1. South Ural State University, Chelyabinsk, Russia

    Timofey Rechkalov & Mikhail Zymbler

Authors
  1. Timofey Rechkalov
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  2. Mikhail Zymbler
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Correspondence to Mikhail Zymbler .

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Editors and Affiliations

  1. South Ural State University, Chelyabinsk, Russia

    Leonid Sokolinsky

  2. South Ural State University, Chelyabinsk, Russia

    Mikhail Zymbler

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Rechkalov, T., Zymbler, M. (2018). A Study of Euclidean Distance Matrix Computation on Intel Many-Core Processors. In: Sokolinsky, L., Zymbler, M. (eds) Parallel Computational Technologies. PCT 2018. Communications in Computer and Information Science, vol 910. Springer, Cham. https://doi.org/10.1007/978-3-319-99673-8_15

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  • DOI: https://doi.org/10.1007/978-3-319-99673-8_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99672-1

  • Online ISBN: 978-3-319-99673-8

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