RT Journal Article
T1 ScrimpCo: scalable matrix profile on commodity heterogeneous processors.
A1 Romero, José Carlos
A1 Vilches Reina, Antonio
A1 Rodríguez-Moreno, Andrés
A1 González-Navarro, María Ángeles
A1 Asenjo-Plaza, Rafael
K1 Computación heterogénea
K1 Análisis de series temporales
K1 Consumo de energía
AB The discovery of time series motifs and discords is considered a paramount and challenging problem regarding time series analysis. In this work, we present ScrimpCo, a heterogeneous implementation of a previous algorithm called SCRIMP that excels at finding relevant subsequences in time series. We propose and evaluate several static, dynamic and adaptive partition strategies targeting commodity processors, on both homogeneous (CPU multicore) and heterogeneous (CPU + GPU) architectures. For the CPU + GPU implementation, we explore a heterogeneous parallel_reduce pattern that computes part of the computation onto an OpenCL capable GPU, whereas the CPU cores take care of the other part. Our heterogeneous scheduler, built on top of TBB, pays special attention to appropriately balance the computational load among the GPU and CPU cores. The experimental results show that our homogeneous implementation scales linearly and that our heterogeneous implementation allows us to reach near-ideal performance on commodity processors that feature an on-chip GPU
PB Springer
YR 2020
FD 2020
LK https://hdl.handle.net/10630/40605
UL https://hdl.handle.net/10630/40605
LA eng
NO Romero, J.C., Vilches, A., Rodríguez, A. et al. ScrimpCo: scalable matrix profile on commodity heterogeneous processors. J Supercomput 76, 9189–9210 (2020)
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 23 ene 2026