Dynamic GPGPU Power Management Using Adaptive Model Predictive Control

Modern processors can greatly increase energy efficiency through techniques such as dynamic voltage and frequency scaling. Traditional predictive schemes are limited in their effectiveness by their inability to plan for the performance and energy characteristics of upcoming phases. To date, there has been little research exploring more proactive techniques that account for expected future behavior when making decisions. This paper proposes using Model Predictive Control (MPC) to attempt to maximize the energy efficiency of GPU kernels without compromising performance. We develop performance and power prediction models for a recent CPU-GPU heterogeneous processor. Our system then dynamically adjusts hardware states based on recent execution history, the pattern of upcoming kernels, and the predicted behavior of those kernels. We also dynamically trade off the performance overhead and the effectiveness of MPC in finding the best configuration by adapting the horizon length at runtime. Our MPC technique limits performance loss by proactively spending energy on the kernel iterations that will gain the most performance from that energy. This energy can then be recovered in future iterations that are less performance sensitive. Our scheme also avoids wasting energy on low-throughput phases when it foresees future high-throughput kernels that could better use that energy. Compared to state-of-the-practice schemes, our approach achieves 24.8% energy savings with a performance loss (including MPC overheads) of 1.8%. Compared to state-of-the-art history-based schemes, our approach achieves 6.6% chip-wide energy savings while simultaneously improving performance by 9.6%.