# The Use of MPI and OpenMP Technologies for Subsequence Similarity Search in Very Large Time Series on Computer Cluster System with Nodes Based on the Intel Xeon Phi Knights Landing Many-core Processor

## TL;DR

This paper presents a scalable parallel algorithm for subsequence similarity search in large time series using MPI and OpenMP on Intel Xeon Phi Knights Landing clusters, optimizing vector computations for improved performance.

## Contribution

It introduces a novel parallel algorithm leveraging MPI and OpenMP for efficient subsequence search on KNL clusters, incorporating data structures and redundant computations for better vectorization.

## Key findings

- Algorithm is highly scalable on real-world datasets
- Effective utilization of vector computations on KNL processors
- Parallelization across cluster nodes improves performance

## Abstract

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.