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.
|Title of host publication||Proceedings of SC 2016|
|Subtitle of host publication||The International Conference for High Performance Computing, Networking, Storage and Analysis|
|Number of pages||11|
|State||Published - Jul 2 2016|
|Event||2016 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2016 - Salt Lake City, United States|
Duration: Nov 13 2016 → Nov 18 2016
|Name||International Conference for High Performance Computing, Networking, Storage and Analysis, SC|
|Conference||2016 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2016|
|City||Salt Lake City|
|Period||11/13/16 → 11/18/16|
Bibliographical noteFunding Information:
The authors would like to thank NVIDiA for providing GPU devices used for experiments. 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).
© 2016 IEEE.
- algorithmic slack prediction
- critical path
- dense matrix factorizations
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Hardware and Architecture