RT Conference Proceedings
T1 Accelerating time series motif discovery in the Intel Xeon Phi KNL processor.
A1 Fernández-Vega, Iván
A1 Villegas Fernández, Alejandro
A1 Gutiérrez-Carrasco, Eladio Damián
A1 Plata-González, Óscar Guillermo
K1 Informática-Congresos
AB Time series analysis is an important research topic of great interest in many fields. However, the memory-bound nature of the state-of-the-art algorithms limits the execution performance in some processor architectures. We analyze the Matrix Profile algorithm from the performance viewpoint in the context of the Intel Xeon Phi Knights Landing architecture (KNL). The experimental evaluation shows a performance improvement up to 190x with respect to the sequential execution and that the use of the HBM memory improves performance in a factor up to 5x with respect to the DDR4 memory.
YR 2020
FD 2020-02-11
LK https://hdl.handle.net/10630/19259
UL https://hdl.handle.net/10630/19259
LA eng
NO Presented at HiPEAC Conference 2020, Bologna (Italy)
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 24 ene 2026