Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/341993
Type: Artigo
Title: High-performance ensembles of online sequential extreme learning machine for regression and time series forecasting
Author: Grim, Luís Fernando L.
Gradvohl, André Leon S.
Abstract: Ensembles of Online Sequential Extreme Learning Machine algorithm are suitable for forecasting Data Streams with Concept Drifts. Nevertheless, data streams forecasting require high-performance implementations due to the high incoming samples rate. In this work, we proposed to tune-up three ensembles, which operates with the Online Sequential Extreme Learning Machine, using high-performance techniques. We reim-plemented them in the C programming language with Intel MKL and MPI libraries. The Intel MKL provides functions that explore the multithread features in multicore CPUs, which expands the parallelism to multiprocessors architectures. The MPI allows us to parallelize tasks with distributed memory on several processes, which can be allocated within a single computational node, or spread over several nodes. In summary, our proposal consists of a two-level parallelization, where we allocated each ensemble model into an MPI process, and we parallelized the internal functions of each model in a set of threads through Intel MKL. Thus, the objective of this work is to verify if our proposals provide a significant improvement in execution time when compared to the respective conventional serial approaches. For the experiments, we used a synthetic and a real dataset. Experimental results showed that, in general, the high-performance ensembles improve the execution time, when compared with its serial version, performing up to 10-fold faster
Subject: Linguagem de programação (Computadores)
Mineração de dados
Aprendizado de máquina
Multitarefa (Computação)
Análise de séries temporais
Fluxo de dados (Computadores)
Country: Estados Unidos
Editor: IEEE Computer Society
Rights: Fechado
Identifier DOI: 10.1109/CAHPC.2018.8645863
Address: https://ieeexplore.ieee.org/abstract/document/8645863
Date Issue: 2019
Appears in Collections:FT - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
2-s2.0-85063145523.pdf292.48 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.