Accurate prediction of NVIDIA GPU power consumption remains challenging due to rapid architectural evolution. Existing machine-learning–based power models are tightly coupled to specific GPU architectures and degrade sharply on unseen platforms, requiring retraining and extensive power measurements, which hinder scalability. This paper presents a quantile-regression–based GPU power prediction framework that enables architecture-agnostic power estimation using static analysis-based compile-time CUDA kernel features. The key insight is that architectural changes primarily induce systematic shifts in power scale, while the relative ordering of kernel power demands remains preserved. By learning power quantiles that capture this ordering and mapping them to new GPUs through one-time calibration, the proposed approach mitigates cross-architecture distribution shift. Extensive evaluation across multiple NVIDIA GPU generations shows that, on unseen architectures, the proposed method improves prediction accuracy by up to 30–50% over existing regression models, while maintaining comparable accuracy in in-distribution settings. The resulting low-overhead, generalizable power estimates make the approach practical for power-aware scheduling, energy budgeting, and sustainability-oriented resource management in large HPC systems.

As GPUs become increasingly popular in commodity hardware as well as High Performance Computing(HPC) systems, the need for sustainable computing is more critical. This work addresses the challenge of identifying the optimal operating power for GPUs to minimize energy consumption and operational temperature while incurring only minimal performance overhead. We propose a machine learning-based solution that leverages tree-based models to predict the optimal GPU power cap using key system parameters, including GPU utilization, Memory utilization, Temperature, and Frequency. Our experimental results demonstrate that our model can achieve a maximum energy saving of 12. 87% and a temperature reduction of 11. 38%, with only a 2.69% increase in execution time. These findings highlight the potential of our approach to enhance energy efficiency and thermal management in GPU-based systems, paving the way for more sustainable computing practices.

As Graphic Processing Units (GPU)s play an increasingly important role in High-Performance Computing (HPC) and data-intensive Machine Learning (ML) tasks, accurate power prediction is essential. Traditional methods, relying on architecture-specific models like DVFS and hardware counters, limit cross-architecture applicability of these models. We propose a static analysis framework that predicts an application's power usage across different NVIDIA GPU architectures without execution. Extensive experiments with state-of-the-art ML approaches show promising results, demonstrating generalizability in predicting power consumption for a newer architecture without the need for complete retraining.11This research is partially supported by the New Faculty Seed Grant of BITS Pilani under Grant No.NFSG/GOA/2023/G0916.