Issues on GPU Parallel Implementation of Evolutionary High-Dimensional Multi-objective Feature Selection

The interest on applications that analyse large volumes of high dimensional data has grown recently. Many of these applications related to the so-called Big Data show different implicit parallelism that can benefit from the efficient use, in terms of performance and power consumption, of Graphics Processing Unit (GPU) accelerators. Although the GPU microarchitectures make possible the acceleration of applications by exploiting parallelism at different levels, the characteristics of their memory hierarchy and the location of GPUs as coprocessors require a careful organization of the memory access patterns and data transferences to get efficient speedups. This paper aims to take advantage of heterogeneous parallel codes on GPUs to accelerate evolutionary approaches in Electroencephalogram (EEG) classification and feature selection in the context of Brain Computer Interface (BCI) tasks. The results show the benefits of taking into account not only the data parallelism achievable by GPUs, but also the memory access patterns, in order to increase the speedups achieved by superscalar cores.