# Sustaining Performance While Reducing Energy Consumption: A Control Theory Approach

arXiv: https://arxiv.org/abs/2107.02426 · HTML: https://mlsanity.com/paper/2107.02426/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2021.

## Built on by (most cited first, 2 of 2)

- [A Reinforcement Learning Approach for Performance-aware Reduction in Power Consumption of Data Center Compute Nodes](https://mlsanity.com/paper/2308.08069/) (2023)
- [Offline Reinforcement-Learning-Based Power Control for Application-Agnostic Energy Efficiency](https://mlsanity.com/paper/2601.11352/) (2026)

## Abstract (arXiv)

Production high-performance computing systems continue to grow in complexity and size. As applications struggle to make use of increasingly heterogeneous compute nodes, maintaining high efficiency (performance per watt) for the whole platform becomes a challenge. Alongside the growing complexity of scientific workloads, this extreme heterogeneity is also an opportunity: as applications dynamically undergo variations in workload, due to phases or data/compute movement between devices, one can dynamically adjust power across compute elements to save energy without impacting performance. With an aim toward an autonomous and dynamic power management strategy for current and future HPC architectures, this paper explores the use of control theory for the design of a dynamic power regulation method. Structured as a feedback loop, our approach-which is novel in computing resource management-consists of periodically monitoring application progress and choosing at runtime a suitable power cap for processors. Thanks to a preliminary offline identification process, we derive a model of the dynamics of the system and a proportional-integral (PI) controller. We evaluate our approach on top of an existing resource management framework, the Argo Node Resource Manager, deployed on several clusters of Grid'5000, using a standard memory-bound HPC benchmark.