research-article

Multi-objective feature selection for EEG classification with multi-level parallelism on heterogeneous CPU-GPU clusters

Authors:

Juan José Escobar,

Antonio Francisco Díaz,

Jesús González,

Miguel DamasAuthors Info & Claims

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1862 - 1869

https://doi.org/10.1145/3205651.3208239

Published: 06 July 2018 Publication History

Abstract

The present trend in the development of computer architectures that offer improvements in both performance and energy efficiency has provided clusters with interconnected nodes including multiple multi-core microprocessors and accelerators. In these so-called heterogeneous computers, the applications can take advantage of different parallelism levels according to the characteristics of the architectures in the platform. Thus, the applications should be properly programmed to reach good efficiencies, not only with respect to the achieved speedups but also taking into account the issues related to energy consumption. In this paper we provide a multi-objective evolutionary algorithm for feature selection in electroencephalogram (EEG) classification, which can take advantage of parallelism at multiple levels: among the CPU-GPU nodes interconnected in the cluster (through message-passing), and inside these nodes (through shared-memory thread-level parallelism in the CPU cores, and data-level parallelism and thread-level parallelism in the GPU). The procedure has been experimentally evaluated in performance and energy consumption and shows statistically significant benefits for feature selection: speedups of up to 73 requiring only a 6% of the energy consumed by the sequential code.

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Cited By

Vázquez Noguera JMello-Román JPinto-Roa DGomez SAyala JAquino-Brítez DGardel-Sotomayor PGarcía-Torres MFacon JCastillo Benítez VMatto IPérez-Estigarribia P(2023)Multiclass Diabetic Retinopathy Classification of Eye Fundus Images Small Datasets Performance Improvement – A Neuroevolution Approach2023 XLIX Latin American Computer Conference (CLEI)10.1109/CLEI60451.2023.10346184(1-10)Online publication date: 16-Oct-2023
https://doi.org/10.1109/CLEI60451.2023.10346184
Escobar JOrtega JDíaz AGonzález JDamas M(2019)Time-energy analysis of multilevel parallelism in heterogeneous clustersThe Journal of Supercomputing10.1007/s11227-019-02908-475:7(3397-3425)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s11227-019-02908-4
Escobar JOrtega JDíaz AGonzález JDamas M(2018)Speedup and Energy Analysis of EEG Classification for BCI Tasks on CPU-GPU ClustersProceedings of the 6th International Workshop on Parallelism in Bioinformatics10.1145/3235830.3235834(33-43)Online publication date: 23-Sep-2018
https://dl.acm.org/doi/10.1145/3235830.3235834

Index Terms

Multi-objective feature selection for EEG classification with multi-level parallelism on heterogeneous CPU-GPU clusters

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cover image ACM Conferences

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2018

1968 pages

ISBN:9781450357647

DOI:10.1145/3205651

Editor:
Hernan Aguirre
Shinshu University
,
General Chair:
Keiki Takadama
The University of Electro-Communications

Copyright © 2018 ACM.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Published: 06 July 2018

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Spanish "Ministerio de Economia y Competitividad" and ERDF funds

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GECCO '18

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GECCO '18: Genetic and Evolutionary Computation Conference

July 15 - 19, 2018

Kyoto, Japan

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Vázquez Noguera JMello-Román JPinto-Roa DGomez SAyala JAquino-Brítez DGardel-Sotomayor PGarcía-Torres MFacon JCastillo Benítez VMatto IPérez-Estigarribia P(2023)Multiclass Diabetic Retinopathy Classification of Eye Fundus Images Small Datasets Performance Improvement – A Neuroevolution Approach2023 XLIX Latin American Computer Conference (CLEI)10.1109/CLEI60451.2023.10346184(1-10)Online publication date: 16-Oct-2023
https://doi.org/10.1109/CLEI60451.2023.10346184
Escobar JOrtega JDíaz AGonzález JDamas M(2019)Time-energy analysis of multilevel parallelism in heterogeneous clustersThe Journal of Supercomputing10.1007/s11227-019-02908-475:7(3397-3425)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s11227-019-02908-4
Escobar JOrtega JDíaz AGonzález JDamas M(2018)Speedup and Energy Analysis of EEG Classification for BCI Tasks on CPU-GPU ClustersProceedings of the 6th International Workshop on Parallelism in Bioinformatics10.1145/3235830.3235834(33-43)Online publication date: 23-Sep-2018
https://dl.acm.org/doi/10.1145/3235830.3235834

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