Balancing Energy Efficiency and Real-Time Performance in GPU Scheduling

Abstract

General-purpose graphics processing units (GPUs) made available on embedded platforms have gained much interest in real-time cyber-physical systems. Despite the fact that GPUs generally outperform CPUs on many compute-intensive tasks in a multitasking environment, high power consumption remains a challenging problem. In this paper, we first analyze the power and energy consumption of GPU kernels scheduled with spatial multitasking, which is found to be advantageous for schedulability in recent studies, and prove that its use, however, degrades energy efficiency even in the latest commercially available embedded GPUs like NVIDIA Jetson Xavier AGX. Then, based on our observations, we propose sBEET, a real-time energy-efficient GPU scheduling framework that makes scheduling decisions at runtime to optimize the energy consumption while utilizing spatial multitasking to improve real-time performance. We evaluate the performance of the proposed sBEET framework using well-known GPU benchmarks and randomly-generated timing parameters on real hardware. The results indicate that sBEET reduces deadline misses up to 13% when the system is overloaded, and also achieves 15% to 21% lower energy consumption when the tasksets are schedulable compared to the existing works.