LPV Control for Dynamic Power Capping in High-Performance Computing under Mixed Workloads

2026-08-04

AI Summary

This work addresses the challenge that static power constraints in high-performance computing systems struggle to adapt to the dynamic nature of mixed workloads. To overcome this limitation, the paper proposes a polytopic linear parameter-varying (LPV) H∞ feedback control approach based on workload phase identification. The method employs memory/compute phases as scheduling variables, integrating gain-scheduled PI control with polytopic LPV modeling and optimizing controller performance via H∞ synthesis. Experimental results demonstrate that, under practical power constraints, the proposed LPV controller significantly improves tracking accuracy of the power budget, enhances robustness, and ensures smoother transient behavior during phase transitions compared to conventional gain-scheduled PI controllers.

Abstract

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.

Problem

• power capping
• high-performance computing
• mixed workloads
• dynamic power regulation
• energy-performance trade-off

Innovation

• LPV control
• dynamic power capping
• mixed workloads
• H-infinity synthesis
• gain scheduling