Power consumption is emerging as one of the main concerns in the High Performance Computing (HPC) field. As a growing number of bioinformatics applications require HPC techniques and parallel architectures to meet performance requirements, power consumption arises as an additional limitation when accelerating them. In this paper, we present a comparative study of optimized implementations of the Non-negative Matrix Factorization (NMF), that is widely used in many fields of bioinformatics, taking into account both performance and power consumption. We target a wide range of state-of-the-art parallel architectures, including general-purpose, low-power processors and specific-purpose accelerators like GPUs, DSPs or the Intel Xeon Phi. From our study, we gain insights in both performance and energy consumption for each one of them under a number of experimental conditions, and conclude that the most appropriate architecture is usually a trade-off between performance and energy consumption for a given experimental setup and dataset. 
Non-negative Matrix Factorization on Low-Power Architectures and Accelerators: A Comparative Study