A parallel discord discovery algorithm for time series on many-core accelerators

Authors

  • M.L. Zymbler South Ural State University

DOI:

https://doi.org/10.26089/NumMet.v20r320

Keywords:

time series, discord discovery, parallel algorithm, vectorization, OpenMP, OpenAcc, Intel Xeon Phi, NVIDIA GPU

Abstract

Discord is a refinement of the concept of anomalous subsequence of a time series. The discord discovery problem frequently occurs in a wide range of application areas related to time series: medicine, economics, climate modeling, etc. In this paper we propose a new parallel discord discovery algorithm for many-core systems in the case when the input data fit in the main memory. The algorithm exploits the ability to independently calculate the Euclidean distances between the subsequences of the time series. Computations are paralleled using OpenMP and OpenAcc for the Intel MIC (Many Integrated Core) and NVIDIA GPU platforms, respectively. The algorithm consists of two stages, namely precomputations and discovery. At the precomputation stage, we construct the auxiliary matrix data structures to ensure the efficient vectorization of computations on an accelerator. At the discovery stage, the algorithm searches for a discord based on the constructed structures. A number of numerical experiments confirm a high scalability of the proposed algorithm.

Author Biography

M.L. Zymbler

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Published

25-06-2019

How to Cite

Цымблер М.Л. A Parallel Discord Discovery Algorithm for Time Series on Many-Core Accelerators // Numerical Methods and Programming (Vychislitel’nye Metody i Programmirovanie). 2019. 20. 211-223. doi 10.26089/NumMet.v20r320

Issue

Section

Section 1. Numerical methods and applications