Matrix Dimensionality Reduction for Mining Web Logs
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Journal of Biomedical Informatics
Document clustering using nonnegative matrix factorization
Information Processing and Management: an International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Unleashing the high-performance and low-power of multi-core DSPs for general-purpose HPC
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Level-3 BLAS on the TI C6678 Multi-core DSP
SBAC-PAD '12 Proceedings of the 2012 IEEE 24th International Symposium on Computer Architecture and High Performance Computing
| Hi-index | 0.00 |
Power consumption is emerging as one of the main concerns in the High Performance Computing (HPC) field. Many bioinformatics applications require HPC techniques and parallel architectures to meet performance requirements, but at the same time they can be severely limited by energy consumption restrictions. In this paper, we perform an empirical study of an optimized implementation of the Nonnegative Matrix Factorization (NMF), that is widely used in many fields of bioinformatics. We target different types of architectures, including general-purpose, low-power embedded processors and specific-purpose architectures like graphics processors and digital signal processors. From our study, we gain insights in both performance and energy consumption for each one of them under given experimental conditions, and conclude that the most appropriate architecture is usually a trade-off between performance and power consumption for a given experiment and dataset.