Work-in-Progress: Understanding the Effect of Kernel Scheduling on GPU Energy Consumption

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, higher power consumption remains a challenging problem. This paper presents our study on the energy consumption characteristics of an NVIDIA AGX Xavier GPU, the latest commercially available embedded hardware, under different concurrency levels and kernel scheduling orders. Our findings pave the way for designing an energy efficient scheduler for GPUs with real-time guarantees.