Energy-efficient execution of dense linear algebra algorithms on multi-core processors

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.