Thermal-Aware Scheduling for Integrated CPUs--GPU Platforms

As modern embedded systems like cars need high-power integrated CPUs--GPU SoCs for various real-time applications such as lane or pedestrian detection, they face greater thermal problems than before, which may, in turn, incur higher failure rate and cooling cost. We demonstrate, via experimentation on a representative CPUs--GPU platform, the importance of accounting for two distinct thermal characteristics—the platform’s temperature imbalance and different power dissipations of different tasks—in real-time scheduling to avoid any burst of power dissipations while guaranteeing all timing constraints. To achieve this goal, we propose a new <u>R</u>eal-<u>T</u>ime <u>T</u>hermal-<u>A</u>ware <u>S</u>cheduling (RT-TAS) framework. We first capture different CPU cores’ temperatures caused by different GPU power dissipations (i.e., CPUs--GPU thermal coupling) with core-specific thermal coupling coefficients. We then develop thermally-balanced task-to-core assignment and CPUs--GPU co-scheduling. The former addresses the platform’s temperature imbalance by efficiently distributing the thermal load across cores while preserving scheduling feasibility. Building on the thermally-balanced task assignment, the latter cooperatively schedules CPU and GPU computations to avoid simultaneous peak power dissipations on both CPUs and GPU, thus mitigating excessive temperature rises while meeting task deadlines. We have implemented and evaluated RT-TAS on an automotive embedded platform to demonstrate its effectiveness in reducing the maximum temperature by 6−12.2°C over existing approaches without violating any task deadline.