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