SSMDVFS: Microsecond-Scale DVFS on GPGPUs with Supervised and Self-Calibrated ML

Minqing Sun, Ruiqi Sun, Yingtao Shen, Wei Yan, Qinfen Hao, An Zou

Abstract: Over the past decade, as GPUs have evolved to achieve higher computational performance, their power density has also accelerated. Consequently, improving energy efficiency and reducing power consumption has become critically important. Dynamic voltage and frequency scaling (DVFS) is an effective technique for enhancing energy efficiency. With the advent of integrated voltage regulators, DVFS can now operate on microsecond $(\boldsymbol{\mu}\mathbf{s})$ timescales. However, developing a practical and effective strategy to guide rapid DVFS remains a significant challenge. This paper proposes a supervised and self-calibrated machine learning framework (SSMDVFS) to guide microsecond-scale GPU voltage and frequency scaling. This framework features an end-to-end design that encompasses data generation, neural network model design, training, compression, and final runtime calibration. Unlike analytical models, which struggle to accurately represent GPU architectures, and reinforcement learning approaches, which can be challenging to converge during runtime, the SSMDVFS offers a practical solution for guiding microsecond-scale voltage and frequency scaling. Experimental results demonstrate that the proposed framework improves energy-delay product (EDP) by 11.09% and outperforms analytical models and reinforcement learning approaches by 13.17% and 36.80 %, respectively.

External IDs:dblp:conf/date/SunSSYHZ25