2019-03-23T22:34:28Zhttp://repositori.uji.es/oai/requestoai:repositori.uji.es:10234/934532019-01-21T14:26:55Zcom_10234_7036com_10234_9col_10234_8620
00925njm 22002777a 4500
dc
Alonso, Pedro
author
Dolz Zaragozá, Manuel Francisco
author
Mayo, Rafael
author
Quintana Ortí, Enrique S.
author
2013-09
This paper addresses the efficient exploitation of task-level parallelism, present in many dense linear algebra operations, from the point of view of both computational performance and energy consumption. The strategies considered here, referred to as the Slack Reduction Algorithm (SRA) and the Race-to-Idle Algorithm (RIA), adjust the operation frequency of the cores during the execution of a collection of tasks (in which many dense linear algebra algorithms can be decomposed) with very different approaches to save energy. The procedures are evaluated using an energy-aware simulator, which is in charge of scheduling/mapping the execution of these tasks to the cores, leveraging dynamic frequency voltage scaling featured by current technology. Experiments with this tool and the practical integration of the RIA strategy into a runtime show the energy gains for two versions of the QR factorization.
Alonso, P., Dolz, M. F., Mayo, R., & Quintana-Ortí, E. S. (2013). Energy-efficient execution of dense linear algebra algorithms on multi-core processors. Cluster Computing, 16(3), 497-509
1386-7857
1573-7543
http://hdl.handle.net/10234/93453
http://dx.doi.org/10.1007/s10586-012-0215-x
Dense linear algebra
Power consumption
Multi-core processors
DVFS
Energy-efficient execution of dense linear algebra algorithms on multi-core processors