Implementações paralelas para o algoritmo Online Sequential Extreme Learning Machine aplicadas a Previsão de concentração de Material Particulado no ar
Abstract
In this work, we proposed two implementations for the Online Sequential Extreme Learning Machine algorithm in C programming language, one with OpenBLAS and another with MAGMA, both open source libraries. The goal is to compare the performance – forecasting error and real execution time – between the implementations when forecasting concentrations of particulate matter in the air. The results showed that the update block size of the algorithm influences the execution time of each implementation differently.References
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Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M., and Bouchachia, A. (2014). A survey on concept drift adaptation. Computing Surveys, 46(4):1–37.
Honaker, J., King, G., and Blackwell, M. (2011). Amelia II: A program for missing data. Journal of Statistical Software, 45(7):1–47.
Huang, G.-B. (2013). MATLAB Codes of ELM Algorithm.
Krawczyk, B. (2016). GPU-accelerated extreme learning machines for imbalanced data streams with Concept Drift. Procedia Computer Science, 80:1692–1701.
Liang, N.-Y., Huang, G.-B., Saratchandran, P., and Sundararajan, N. (2006). A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks. IEEE Transactions on Neural Networks, 17(6):1411–1423.
NVIDIA (2012). CUDA Toolkit 4.2 CUBLAS Library.
Tomov, S., Dongarra, J., and Baboulin, M. (2010). Towards dense linear algebra for hybrid GPU accelerated manycore systems. Parallel Computing, 36(5-6):232–240.
Published
2018-04-13
How to Cite
GRIM, Luís Fernando L.; BUENO, Andrés; GRADVOHL, André Leon S..
Implementações paralelas para o algoritmo Online Sequential Extreme Learning Machine aplicadas a Previsão de concentração de Material Particulado no ar. In: REGIONAL SCHOOL OF HIGH PERFORMANCE COMPUTING FROM SÃO PAULO (ERAD-SP), 9. , 2018, São José dos Campos.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 73-76.
DOI: https://doi.org/10.5753/eradsp.2018.13606.
