Modeling and Controlling Many-Core HPC Processors: an Alternative to PID and Moving Average Algorithms
<|reference_start|>Modeling and Controlling Many-Core HPC Processors: an Alternative to PID and Moving Average Algorithms: The race towards performance increase and computing power has led to chips with heterogeneous and complex designs, integrating an ever-growing number of cores on the same monolithic chip or chiplet silicon die. Higher integration density, compounded with the slowdown of technology-driven power reduction, implies that power and thermal management become increasingly relevant. Unfortunately, existing research lacks a detailed analysis and modeling of thermal, power, and electrical coupling effects and how they have to be jointly considered to perform dynamic control of complex and heterogeneous Multi-Processor System on Chips (MPSoCs). To close the gap, in this work, we first provide a detailed thermal and power model targeting a modern High Performance Computing (HPC) MPSoC. We consider real-world coupling effects such as actuators' non-idealities and the exponential relation between the dissipated power, the temperature state, and the voltage level in a single processing element. We analyze how these factors affect the control algorithm behavior and the type of challenges that they pose. Based on the analysis, we propose a thermal capping strategy inspired by Fuzzy control theory to replace the state-of-the-art PID controller, as well as a root-finding iterative method to optimally choose the shared voltage value among cores grouped in the same voltage domain. We evaluate the proposed controller with model-in-the-loop and hardware-in-the-loop co-simulations. We show an improvement over state-of-the-art methods of up to 5x the maximum exceeded temperature while providing an average of 3.56% faster application execution runtime across all the evaluation scenarios.<|reference_end|>
@article{bambini2024modeling,
    title={Modeling and Controlling Many-Core HPC Processors: an Alternative to PID
  and Moving Average Algorithms},
    author={Giovanni Bambini, Alessandro Ottaviano, Christian Conficoni, Andrea
  Tilli, Luca Benini, Andrea Bartolini},
    journal={arXiv preprint arXiv:2405.18030},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18030},
    primaryClass={eess.SY cs.PF cs.SY}
}