Adaptive GPU Power Capping: Balancing Energy Efficiency,Thermal Control and Performance

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.