It is challenging to manage the thermal behavior of many-core microprocessors while still keeping them running at high performance since the control complexity increases as the core number increases. In this article, a novel hierarchical dynamic thermal management method is proposed to overcome this challenge. The new method employs model predictive control (MPC) with task migration and a DVFS scheme to ensure smooth control behavior and negligible computing performance sacrifice. In order to be scalable to manycore systems, the hierarchical control scheme is designed with two levels. At the lower level, the cores are spatially clustered into blocks, and local task migration is used to match current power distribution with the optimal distribution calculated by MPC. At the upper level, global task migration is used with the unmatched powers from the lower level. A modified iterative minimum cut algorithm is used to assist the task migration decision making if the power number is large at the upper level. Finally, DVFS is applied to regulate the remaining unmatched powers. Experiments show that the new method outperforms existing methods and is very scalable to manage many-core microprocessors with small performance degradation.

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