Modern real-time systems utilize considerable amounts of power while executing computation-intensive tasks. The execution of these tasks leads to significant power dissipation and heating of the device. It therefore results in severe thermal issues like temperature escalation, high thermal gradients, and excessive hot spot formation, which may result in degrading chip performance, accelerating device aging, and premature failure. Thermal-Aware Scheduling (TAS) enables optimization of thermal dissipation to maintain a safe thermal state. In this work, we implement a new TAS algorithm, POD-TAS, which manages the thermal behavior of a multi-core CPU based on a defined set of states and their transitions. We compare the performances of a dynamic RC thermal circuit simulator and a reduced order Proper Orthogonal Decomposition (POD)-based thermal model and we select the latter for use in our POD-TAS algorithm. We implement a novel simulation-based evaluation methodology to compare TAS algorithms. This methodology is used to evaluate the performance of the proposed POD-TAS algorithm. Additionally, we compare the performance of a state of the art TAS algorithm to our proposed POD-TAS algorithm. Furthermore, we utilize the COMBS benchmark suite to provide CPU workloads for task scheduling. Our evaluation includes scenarios with using 4 and 8 benchmarks. We align our evaluation with the RT-TAS by comparing the 4 benchmark scenario. Additionally, the 8 benchmark case simulates a heavier workload scenario, which puts more thermal stress on the system and is more difficult to mitigate. Our experimental results on a multi-core processor using a set of 4 benchmarks demonstrate that the proposed POD-TAS method can improve thermal performance by decreasing the peak thermal variance by 53.0% and the peak chip temperature of 29.01%. Using a set of 8 benchmarks, the comparison of the two algorithms shows a decrease of 29.57% in the peak spatial variance of the chip temperature and 26.26% in the peak chip temperature. We also identify several potential future research directions.

中文翻译：

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使用降阶学习热模型通过热感知调度调节 CPU 温度


现代实时系统在执行计算密集型任务时会消耗大量电力。执行这些任务会导致器件的大量功率耗散和发热。因此，它会导致严重的热问题，如温度升高、高热梯度和过度热点形成，这可能导致芯片性能下降、加速器件老化和过早失效。热感知调度 （TAS） 支持优化散热，以保持安全的热状态。在这项工作中，我们实现了一种新的 TAS 算法 POD-TAS，该算法根据一组定义的状态及其转换来管理多核 CPU 的热行为。我们比较了动态 RC 热电路仿真器和基于降阶正确正交分解 （POD） 的热模型的性能，并选择后者用于我们的 POD-TAS 算法。我们实施了一种新颖的基于仿真的评估方法来比较 TAS 算法。该方法用于评估所提出的 POD-TAS 算法的性能。此外，我们将最先进的 TAS 算法的性能与我们提出的 POD-TAS 算法进行了比较。此外，我们利用 COMBS 基准测试套件为任务调度提供 CPU 工作负载。我们的评估包括使用 4 个和 8 个基准测试的场景。我们通过比较 4 个基准情景来使我们的评估与 RT-TAS 保持一致。此外，8 基准测试情况模拟了更重的工作负载场景，这会给系统带来更多的热应力，并且更难缓解。 我们使用一组 4 个基准测试在多核处理器上的实验结果表明，所提出的 POD-TAS 方法可以通过降低 53.0% 的峰值热方差和 29.01% 的峰值芯片温度来提高热性能。使用一组 8 个基准测试，两种算法的比较表明，芯片温度的峰值空间方差降低了 29.57%，芯片峰值温度降低了 26.26%。我们还确定了几个潜在的未来研究方向。