Paper Info
Title
Parallel high-dimensional multi-objective feature selection for EEG classification with dynamic workload balancing on CPU–GPU architectures
Abstract
Many bioinformatics applications that analyse large volumes of high-dimensional data comprise complex problems requiring metaheuristics approaches with different types of implicit parallelism. For example, although functional parallelism would be used to accelerate evolutionary algorithms, the fitness evaluation of the population could imply the computation of cost functions with data parallelism. This way, heterogeneous parallel architectures, including central processing unit (CPU) microprocessors with multiple superscalar cores and accelerators such as graphics processing units (GPUs) could be very useful. This paper aims to take advantage of such CPU–GPU heterogeneous architectures to accelerate electroencephalogram classification and feature selection problems by evolutionary multi-objective optimization, in the context of brain computing interface tasks. In this paper, we have used the OpenCL framework to develop parallel master-worker codes implementing an evolutionary multi-objective feature selection procedure in which the individuals of the population are dynamically distributed among the available CPU and GPU cores.
Year
DOI
Venue
2017
https://doi.org/10.1007/s10586-017-0980-7
Cluster Computing
Keywords
Field
DocType
Dynamic task scheduling,Multi-objective EEG classification,Feature selection,GPU,Heterogeneous parallel architectures,Memory access optimization
Graphics,Population,Central processing unit,Implicit parallelism,Evolutionary algorithm,Feature selection,Computer science,Parallel computing,Real-time computing,Data parallelism,Metaheuristic
Journal
Volume
Issue
ISSN
20
3
1386-7857
Citations 
PageRank 
References 
3
0.44
15
Authors
5
Name
Order
Citations
PageRank
Juan José Escobar172.58
J. Ortega294073.05
Jesús González360444.40
M. Damas438733.04
Antonio F. Díaz57415.45