Parallel computing for dimensionality reduction

Abstract

Big data analytics enables to uncover hidden and useful information for better decisions. Our research area covers big data visualization that is based on dimensionality reduction methods. It requires time and resource consuming processes, so in this paper we look for computing methods and environments that enable to execute the tasks and get results faster. In this research we use Random projection method to reduce the dimensions of the initial data. We investigate how parallel computing based on OpenMP and MPI technologies can increase the performance of these dimensionality reduction processes. The results show the significant improvement of performance when executing MPI code on computer cluster. However, the greater number of cores not always leads to higher speed.