School of Electronic Information, Wuhan University, Wuhan 430072, China.
Sensors (Basel). 2023 Jan 28;23(3):1449. doi: 10.3390/s23031449.
As the importance of uncore components, such as shared cache slices and memory controllers, increases in processor architecture, the percentage of uncore power consumption in the overall power consumption of multicore processors rises significantly. To maximize the power efficiency of a multicore processor system, we investigate the uncore frequency scaling (UFS) policy and propose a novel imitation learning-based uncore frequency control policy. This policy performs online learning based on the DAgger algorithm and converts the annotation cost of online aggregation data into fine-tuning of the expert model. This design optimizes the online learning efficiency and improves the generality of the UFS policy on unseen loads. On the other hand, we shift our policy optimization target to Performance Per Watt (PPW), i.e., the power efficiency of the processor, to avoid saving a percentage of power while losing a larger percentage of performance. The experimental results show that our proposed policy outperforms the current advanced UFS policy in the benchmark test sequence of SPEC CPU2017. Our policy has a maximum improvement of about 10% relative to the performance-first policies. In the unseen processor load, the tuning decision made by our policy after collecting 50 aggregation data can maintain the processor stably near the optimal power efficiency state.
随着处理器架构中核心组件（如共享缓存片和内存控制器）的重要性不断增加，多核处理器的总功耗中核心组件的功耗占比显著上升。为了最大限度地提高多核处理器系统的功率效率，我们研究了非核心频率缩放（UFS）策略，并提出了一种新的基于模仿学习的非核心频率控制策略。该策略基于 DAgger 算法进行在线学习，并将在线聚合数据的注释成本转换为专家模型的微调。这种设计优化了在线学习效率，并提高了 UFS 策略在未见负载下的通用性。另一方面，我们将策略优化目标转移到 Performance Per Watt (PPW)，即处理器的功率效率，以避免在节省一定百分比功率的同时损失更大百分比的性能。实验结果表明，我们提出的策略在 SPEC CPU2017 的基准测试序列中优于当前先进的 UFS 策略。与性能优先策略相比，我们的策略最大可提高约 10%。在未见的处理器负载下，我们的策略在收集 50 个聚合数据后做出的调整决策可以使处理器稳定地保持在最优功率效率状态附近。
