Paper Info
Title
Multi-objective feature selection for EEG classification with multi-level parallelism on heterogeneous CPU-GPU clusters.
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.
Year
Venue
Field
2018
GECCO (Companion)
Cluster (physics),Central processing unit,Eeg classification,Evolutionary algorithm,Feature selection,Computer science,Efficient energy use,Parallel computing,Artificial intelligence,Energy consumption,Machine learning
DocType
ISBN
Citations 
Conference
978-1-4503-5764-7
0
PageRank 
References 
Authors
0.34
9
5
Name
Order
Citations
PageRank
Juan José Escobar163.24
julio ortega2197.89
Antonio F. Díaz37415.45
Jesús González410716.48
M. Damas538733.04