Offline Reinforcement-Learning-Based Power Control for Application-Agnostic Energy Efficiency

TL;DR

This paper proposes an offline reinforcement learning approach for autonomous CPU power control that improves energy efficiency during runtime without significant performance loss, addressing challenges of online RL training.

Contribution

It introduces an offline RL method for energy-efficient CPU power management that combines application-agnostic data and hardware counters, enabling effective control without online training issues.

Findings

Substantial energy savings achieved on various benchmarks

Limited performance degradation during power control

Effective control demonstrated on live systems using Intel's RAPL

Abstract

Energy efficiency has become an integral aspect of modern computing infrastructure design, impacting the performance, cost, scalability, and durability of production systems. The incorporation of power actuation and sensing capabilities in CPU designs is indicative of this, enabling the deployment of system software that can actively monitor and adjust energy consumption and performance at runtime. While reinforcement learning (RL) would seem ideal for the design of such energy efficiency control systems, online training presents challenges ranging from the lack of proper models for setting up an adequate simulated environment, to perturbation (noise) and reliability issues, if training is deployed on a live system. In this paper we discuss the use of offline reinforcement learning as an alternative approach for the design of an autonomous CPU power controller, with the goal of improving the energy efficiency of parallel applications at runtime without unduly impacting their performance. Offline RL sidesteps the issues incurred by online RL training by leveraging a dataset of state transitions collected from arbitrary policies prior to training. Our methodology applies offline RL to a gray-box approach to energy efficiency, combining online application-agnostic performance data (e.g., heartbeats) and hardware performance counters to ensure that the scientific objectives are met with limited performance degradation. Evaluating our method on a variety of compute-bound and memory-bound benchmarks and controlling power on a live system through Intel's Running Average Power Limit, we demonstrate that such an offline-trained agent can substantially reduce energy consumption at a tolerable performance degradation cost.