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Discord is a refinement of the concept of anomalous subsequence of a timeseries. The task of discords discovery is applied in a wide range of subjectdomains related to time series: medicine, economics, climate modeling, etc. Inthis paper, we propose a novel parallel algorithm for discords discovery forthe Intel Xeon Phi Knights Landing (KNL) many-core systems for the case wheninput data fit in main memory. The algorithm exploits the ability toindependently calculate Euclidean distances between the subsequences of thetime series. Computations are paralleled through OpenMP technology. Thealgorithm consists of two stages, namely precomputations and discovery. At theprecomputations stage, we construct the auxiliary matrix data structures, whichensure efficient vectorization of computations on Intel Xeon Phi KNL. At thediscovery stage, the algorithm finds discord based upon the structures above.Experimental evaluation confirms the high scalability of the developedalgorithm.

Parallel Algorithm for Time Series Discords Discovery on the Intel Xeon Phi Knights Landing Many-core Processor