A Physics-Aware Framework for Short-Term GPU Power Forecasting of AI Data Centers

TL;DR

This paper introduces PI-DLinear, a physics-informed time-series model that accurately forecasts short-term GPU power usage in AI data centers by integrating thermal physics with power consumption data.

Contribution

The paper presents the first physics-informed DLinear model for short-term GPU power forecasting, improving accuracy and physical consistency over existing models.

Findings

PI-DLinear outperforms state-of-the-art models in accuracy metrics.

The model respects physical laws during load transients and power throttling.

Forecasting accuracy improvements range from 0.782% to 51.82%.

Abstract

AI data centers experience rapid fluctuations in power demand due to the heterogeneity of computational tasks that they have to support. For example, the power profile of inference and training of large language models (LLMs) is quite distinct and big divergences can result in the instability of the underlying electricity grid. In this paper we propose, to the best of our knowledge, the first physics-informed DLinear time-series model that can accurately forecast power utilization of an AI data center 5-80 minutes (short-term forecasting) into the future. The physics, based on a multi-node lumped thermal resistance-capacitance (RC) network consistent with Newton's law of cooling, is captured using newly derived time-dependent ordinary differential equations (ODE) that separately models and interlinks power consumption with the GPU compute and memory utilization and temperature. The resulting model, that we refer to as PI-DLinear, trained and evaluated on a real AI data center dataset and is not only more accurate than the state-of-the-art (SOTA) models tested, but the forecast profile respects the underlying physics under power throttling and load transient events. Relative to the SOTA transformer-based and non-transformer-based models, improvements in forecasting accuracy (averaged across all look-back and prediction windows) range from 0.782%-39.08% for MSE, 0.993%-51.82% for MAE, and 0.370%-22.28% for RMSE.