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

  • Timofey Rechkalov
  • Mikhail Zymbler
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 910)

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.

Keywords

Euclidean distance matrix OpenMP Intel Xeon Phi Data layout Vectorization 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.South Ural State UniversityChelyabinskRussia

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