Please use this identifier to cite or link to this item:
|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
Análise de séries temporais
Fluxo de dados (Computadores)
|Editor:||IEEE Computer Society|
|Appears in Collections:||FT - Artigos e Outros Documentos|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.