Workload-aware Power Optimization Strategy for Asymmetric Multiprocessors

Abstract

Asymmetric multi-core architectures, such as the ARM big.LITTLE, are emerging as successful solutions for the embedded and mobile markets due to their capabilities to trade-off performance and power consumption. However, both the Heterogeneous Multi-Processing (HMP) scheduler integrated in the commercial products and the previous research approaches are not able to fully exploit such potentiality. We propose a new runtime resource management policy for the big. LITTLE architecture integrated in Linux aimed at optimizing the power consumption while fulfilling performance requirements specified for the running applications. Experimental results show an improvement of the 11% on the performance and at the same time 8% in peak power consumption w.r.t. the current Linux HMP solution.