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

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

Authors

  • M.L. Zymbler

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.


Published

2019-06-25

Issue

Section

Section 1. Numerical methods and applications

Author Biography

M.L. Zymbler


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