Improving Memory Accesses for Heterogeneous Parallel Multi-objective Feature Selection on EEG Classification

Bioinformatics applications that analyze large volumes of high-dimensional data and present different implicit parallelism can benefit from the efficient use, in performance terms, of heterogeneous parallel architectures, including accelerators such as graphics processing units (GPUs). This paper aims to take advantage of parallel codes to accelerate electroencephalogram (EEG) classification and feature selection problems in the context of Branch-Computing Interface (BCI) tasks. As the approaches to tackle these applications usually involve optimized codes that implement different types of parallelism, the use of heterogeneous architectures with multicore microprocessors along with GPUs could provide relevant performance improvements after careful code optimizing. More specifically, the memory access patterns have been taken into account to improve the performance of data-parallel GPU kernels.