  Nowadays, subsequence similarity search is required in a wide range of time
series mining applications: climate modeling, financial forecasts, medical
research, etc. In most of these applications, the Dynamic TimeWarping (DTW)
similarity measure is used since DTW is empirically confirmed as one of the
best similarity measure for most subject domains. Since the DTW measure has a
quadratic computational complexity w.r.t. the length of query subsequence, a
number of parallel algorithms for various many-core architectures have been
developed, namely FPGA, GPU, and Intel MIC. In this article, we propose a new
parallel algorithm for subsequence similarity search in very large time series
on computer cluster systems with nodes based on Intel Xeon Phi Knights Landing
(KNL) many-core processors. Computations are parallelized on two levels as
follows: through MPI at the level of all cluster nodes, and through OpenMP
within one cluster node. The algorithm involves additional data structures and
redundant computations, which make it possible to effectively use the
capabilities of vector computations on Phi KNL. Experimental evaluation of the
algorithm on real-world and synthetic datasets shows that it is highly
scalable.
