A Fast High-Level Event-Driven Thermal Estimator for Dynamic Thermal Aware Scheduling

Thermal aware scheduling (TAS) is an important system level optimization for many-core systems. A fast event driven thermal estimation method, which includes both the dynamic and leakage power models, for monitoring temperature and guiding dynamic TAS (DTAS) is proposed in this paper. The fast event driven thermal estimation is based upon a thermal map, with occasional thermal sensor-based calibration, which is updated only when a high level event occurs. To minimize the overhead, while maintaining the estimation accuracy, prebuilt look-up-tables and predefined leakage calibration parameters are used to speed up the thermal solution. Experimental results show our method is accurate, producing thermal estimations of similar quality to an existing open-source thermal simulator, while having a considerably reduced computational complexity. Based on this predictive approach, we take full advantage of a projected future thermal map to develop several heuristic policies for DTAS. We show that our proposed predictive policies are significantly better, in terms of minimizing average/peak temperature, reducing the dynamic thermal management overhead and improving other real-time features, than existing DTAS schedulers, making them highly suitable for heuristically guiding thermal aware task allocation and scheduling.