Improving the energy efficiency of SMACOF for multidimensional scaling on modern architectures

Abstract

The reduction of the dimensionality is of great interest in the context of big data processing. Multidimensional scaling methods (MDS) are techniques for dimensionality reduction, where data from a high-dimensional space are mapped into a lower-dimensional space. Such methods consume relevant computational resources; therefore, intensive research has been developed to accelerate them. In this work, two efficient parallel versions of the well-known and precise SMACOF algorithm to solve MDS problems have been developed and evaluated on multicore and GPU. To help the user of SMACOF, we provide these parallel versions and a complementary Python code based on a heuristic approach to explore the optimal configuration of the parallel SMACOF algorithm on the available platforms in terms of energy efficiency (GFLOPs/watt). Three platforms, 64 and 12 CPU-cores and a GPU device, have been considered for the experimental evaluation.