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

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

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  • (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
  • (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
  • (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

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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
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 06 July 2018

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Author Tags

  1. EEG multi-objective feature selection
  2. distributed master-worker procedure
  3. energy-aware computing
  4. heterogeneous platform
  5. parallel programming
  6. subpopulation-based genetic algorithm

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  • Spanish "Ministerio de Economia y Competitividad" and ERDF funds

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View all
  • (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
  • (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
  • (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

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