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 (EEG) classification and feature selection problems by evolutionary multi-objective optimization, in the context of Brain Computing Interface (BCI) 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.