RT Journal Article
T1 Time series analysis acceleration with advanced vectorization extensions
A1 Quislant-del-Barrio, Ricardo
A1 Fernández-Vega, Iván
A1 Gutiérrez-Carrasco, Eladio Damián
A1 Plata-González, Óscar Guillermo
K1 Proceso de vectores (Informática)
AB Time series analysis is an important research topic and a key step in monitoring and predicting events in many felds. Recently, the Matrix Profle method, and particularly two of its Euclidean-distance-based implementations—SCRIMP and SCAMP—have become the state-of-the-art approaches in this feld. 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 profle is embarrassingly parallelizable, we fnd that auto-vectorization techniques fail to fully exploit the SIMD capabilities of modern CPUarchitectures. In this paper, we develop custom-vectorized SCRIMP and SCAMP implementations based on AVX2 and AVX-512 extensions, which we combine with multithreading 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 auto-vectorization.
PB Springer
YR 2023
FD 2023
LK https://hdl.handle.net/10630/26387
UL https://hdl.handle.net/10630/26387
LA eng
NO Quislant, R., Fernandez, I., Gutierrez, E. et al. Time series analysis acceleration with advanced vectorization extensions. J Supercomput (2023). https://doi.org/10.1007/s11227-023-05060-2
NO Funding for open access publishing: Universidad Málaga/CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026