Discovery of Time Series Motifs on Intel Many-Core Systems

A motif is a pair of subsequences of a longer time series, which are very similar to each other. Motif discovery is applied in a wide range of subject areas involving time series: medicine, biology, entertainment, weather prediction, and others. In this paper, we propose a novel parallel algorithm f...

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Bibliographic Details
Published inLobachevskii journal of mathematics Vol. 40; no. 12; pp. 2124 - 2132
Main Authors Zymbler, M. L., Kraeva, Ya. A.
Format Journal Article
LanguageEnglish
Published Moscow Pleiades Publishing 01.12.2019
Springer Nature B.V
Subjects
Accelerators
Algebra
Algorithms
Analysis
Data structures
Geometry
Mathematical Logic and Foundations
Mathematics
Mathematics and Statistics
Parallel processing
Probability Theory and Stochastic Processes
Time series
Vector processing (computers)
Weather forecasting
data layout
vectorization
time series
motif discovery
OpenMP
Intel Xeon Phi
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Summary:A motif is a pair of subsequences of a longer time series, which are very similar to each other. Motif discovery is applied in a wide range of subject areas involving time series: medicine, biology, entertainment, weather prediction, and others. In this paper, we propose a novel parallel algorithm for motif discovery using Intel MIC (Many Integrated Core) accelerators in the case when time series fit in the main memory. We perform parallelization through thread-level parallelism and OpenMP technology. The algorithm employs a set of matrix data structures to store and index the subsequences of a time series and to provide an efficient vectorization of computations on the Intel MIC platform. The experimental evaluation shows the high scalability of the proposed algorithm.
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ISSN:1995-0802
1818-9962
DOI:10.1134/S199508021912014X