AI 中文总结
针对混合负载下高性能计算的功率平衡难题，提出增益调度PI与LPV两种反馈控制器，经实验验证LPV控制器在跟踪误差、控制方差及瞬态响应上表现更优。
AI 中文摘要
在高性能计算（HPC）系统中，平衡能耗与性能仍是一项关键挑战。尽管英特尔的运行平均功率限制（RAPL）等静态功率上限机制提供了基础控制能力，但它们缺乏适应动态变化负载的灵活性。本研究针对混合负载场景开展动态功率调节研究，考察两种反馈策略：通过H∞控制合成的增益调度比例积分（PI）控制器与多面体线性参数变分（LPV）控制器，二者均由在内存与计算阶段间切换的负载指标进行调度。我们在实际功率上限约束下评估跟踪性能、阶段切换能力与鲁棒性。两种控制器均满足功率限制，而LPV设计相比增益调度PI控制器，实现了更低的跟踪误差、更小的控制方差，以及阶段切换期间更平滑的瞬态响应。
英文摘要
Balancing energy consumption and performance remains a critical challenge in High Performance Computing (HPC) systems. While static power capping mechanisms such as Intel's Running Average Power Limit (RAPL) offer basic control capabilities, they lack the flexibility to adapt to dynamically varying workloads. This work studies dynamic power regulation for mixed workload scenarios. We investigate two feedback strategies: a gain scheduled proportional-integral (PI) controller and a polytopic linear parameter-varying (LPV) controller synthesized via H$\infty$ control, both scheduled by a workload indicator that changes between memory and compute phase. We evaluate tracking performance, phase switching, and robustness under practical power cap constraints. While both controllers respect power limits, the LPV design achieves lower tracking error, lower control variance, and smoother transients during phase changes than gain scheduled PI.
