@inproceedings{3db4d74e5a444663a728809517302cc4,
title = "GreenLA: Green Linear Algebra Software for GPU-accelerated Heterogeneous Computing",
abstract = "While many linear algebra libraries have been developed to optimize their performance, no linear algebra library considers their energy efficiency at the library design time. In this paper, we present GreenLA- A n energy efficient linear algebra software package that leverages linear algebra algorithmic characteristics to maximize energy savings with negligible overhead. GreenLA is (1) energy efficient: It saves up to several times more energy than the best existing energy saving approaches that do not modify library source codes; (2) high performance: Its performance is comparable to the highly optimized linear algebra library MAGMA; and (3) transparent to applications: With the same programming interface, existing MAGMA users do not need to modify their source codes to benefit from GreenLA. Experimental results demonstrate that GreenLA is able to save up to three times more energy than the best existing energy saving approaches while delivering similar performance compared to the state-of-the-art linear algebra library MAGMA.",
keywords = "CPU, DVFS, GPU, algorithmic slack prediction, critical path, dense matrix factorizations, energy, performance",
author = "Jieyang Chen and Li Tan and Panruo Wu and Dingwen Tao and Hongbo Li and Xin Liang and Sihuan Li and Rong Ge and Laxmi Bhuyan and Zizhong Chen",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2016 ; Conference date: 13-11-2016 Through 18-11-2016",
year = "2016",
month = jul,
day = "2",
doi = "10.1109/SC.2016.56",
language = "English",
series = "International Conference for High Performance Computing, Networking, Storage and Analysis, SC",
publisher = "IEEE Computer Society",
pages = "667--677",
booktitle = "Proceedings of SC 2016",
}
