RT Conference Proceedings
T1 Exploiting Vector Extensions to Accelerate Time Series Analysis.
A1 Quislant-del-Barrio, Ricardo
A1 Fernández-Vega, Iván
A1 Serralvo, Eduardo
A1 Gutiérrez-Carrasco, Eladio Damián
A1 Plata-González, Óscar Guillermo
K1 Análisis de series temporales - Congresos
K1 Algoritmos - Congresos
K1 Arquitectura de ordenadores - Congresos
AB Time series analysis is an important research topic and a key step in monitoring and predicting events in many fields. Recently, the Matrix Profile method, and particularly two of its Euclidean-distance-based implementations – SCRIMP and SCAMP – have become the state-of-the-art approaches in this field. Those algorithms bring the possibility of obtaining exact motifs and discords from a time series, which can be used to infer events, predict outcomes, detect anomalies and more. While matrix profile is embarrassingly parallelizable, we find that autovectorization techniques fail to fully exploit the SIMD capabilities of modern CPU architectures. In this paper, we develop custom-vectorized SCRIMP and SCAMP implementations based on AVX2 and AVX-512 extensions, which we combine with multi-threading techniques aimed at exploiting the potential of the underneath architectures. Our experimental evaluation, conducted using real data, shows a performance improvement of more than 4× with respect to the autovectorization.
PB SARTECO
YR 2022
FD 2022-09-21
LK https://hdl.handle.net/10630/25089
UL https://hdl.handle.net/10630/25089
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026