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

## TL;DR

This paper introduces a parallel algorithm optimized for Intel Xeon Phi KNL processors to efficiently discover time series discords, leveraging vectorization and parallelization techniques.

## Contribution

The paper presents a novel parallel algorithm for time series discords discovery that exploits the architecture of Intel Xeon Phi KNL systems.

## Key findings

- High scalability demonstrated through experiments
- Efficient vectorization achieved with auxiliary data structures
- Algorithm suitable for in-memory large-scale time series analysis

## Abstract

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.