Modular Reinforcement Learning for Self-Adaptive Energy Efficiency Optimization in Multicore System

Energy-efficiency is becoming increasingly important to modern computing systems with multi-/many-core architectures. Dynamic Voltage and Frequency Scaling (DVFS), as an effective low-power technique, has been widely applied to improve energy-efficiency in commercial multi-core systems. However, due to the large number of cores and growing complexity of emerging applications, it is difficult to efficiently find a globally optimized voltage/frequency assignment at runtime. In order to improve the energy-efficiency for the overall multicore system, we propose an online DVFS control strategy based on core-level Modular Reinforcement Learning (MRL) to adaptively select appropriate operating frequencies for each individual core. Instead of focusing solely on the local core conditions, MRL is able to make comprehensive decisions by considering the running-states of multiple cores without incurring exponential memory cost which is necessary in traditional Monolithic Reinforcement Learning (RL). Experimental results on various realistic applications and different system scales show that the proposed approach improves up to 28% energy-efficiency compared to the recent individual-RL approach.