A parallel discord discovery algorithm for time series on many-core accelerators
M. L. Tsymbler
South Ural State University, Chelyabinsk
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.
time series, discord discovery, parallel algorithm, vectorization, OpenMP, OpenAcc, Intel Xeon Phi, NVIDIA GPU.
PDF file (551 kB)
004.272.25; 004.421; 004.032.24
M. L. Tsymbler, “A parallel discord discovery algorithm for time series on many-core accelerators”, Num. Meth. Prog., 20:3 (2019), 211–223
Citation in format AMSBIB
\paper A parallel discord discovery algorithm for time series on many-core accelerators
\jour Num. Meth. Prog.
Citing articles on Google Scholar:
Related articles on Google Scholar:
|Number of views:|