Learning the optimal operating point for many-core systems with extended range voltage/frequency scaling

Near-Threshold Computing (NTC) has emerged as a solution that promises to significantly increase the energy efficiency of next-generation multi-core systems. This paper evaluates and analyzes the behavior of dynamic voltage and frequency scaling (DVFS) control algorithms for multi-core systems operating under near-threshold, nominal, or turbo-mode conditions. We adapt the model selection technique from machine learning to learn the relationship between performance and power. The theoretical results show that the resulting models satisfy convexity properties essential to efficiently determining optimal voltage/frequency operating points for minimizing energy consumption under throughput constraints or maximizing throughput under a given power budget. Our experimental results show that, compared with DVFS in the conventional operating range, extended range DVFS control including turbo-mode and near-threshold operation achieves an additional (1) 13.28% average energy reduction under isoperformance conditions, and (2) 7.54% average throughput increase under iso-power conditions.

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