GreenLA: Green Linear Algebra Software for GPU-Accelerated Heterogeneous Computing


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.

Meeting Name

International Conference for High Performance Computing, Networking, Storage and Analysis, SC '16 (2016: Nov. 13-18, Salt Lake City, UT)


Computer Science


This work is partially supported by the NSF grants CCF-1305622, ACI-1305624, CCF-1513201, CCF-1551511, the SZSTI basic research program JCYJ20150630114942313, and the Special Program for Applied Research on Super Computation of the NSFC Guangdong Joint Fund (the second phase).

Keywords and Phrases

Algorithmic Slack Prediction; CPU; Critical Path; Dense Matrix Factorizations; DVFS; Energy; GPU; Performance

International Standard Book Number (ISBN)


International Standard Serial Number (ISSN)

2167-4329; 2167-4337

Document Type

Article - Conference proceedings

Document Version


File Type





© 2016 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

02 Jul 2016