Scheduling Tasks with Mixed Timing Constraints in GPU-Powered Real-Time Systems

Due to the cost-effective, massive computational power of graphics processing units (GPUs), there is a growing interest of utilizing GPUs in real-time systems. For example GPUs have been applied to automotive systems to enable new advanced and intelligent driver assistance technologies, accelerating the path to self-driving cars. In such systems, GPUs are shared among tasks with mixed timing constraints: real-time (RT) tasks that have to be accomplished before specified deadlines, and non-real-time, best-effort (BE) tasks. In this paper, (1) we propose resource-aware non-uniform slack distribution to enhance the schedulability of RT tasks (the total amount of work of RT tasks whose deadlines can be satisfied on a given amount of resources) in GPU-enabled systems; (2) we propose deadline-aware dynamic GPU partitioning to allow RT and BE tasks to run on a GPU simultaneously, such that BE tasks are not blocked for a long time.

We evaluate the effectiveness of the proposed approaches by using both synthetic benchmarks and a real-world workload that consists of a set of emerging automotive tasks. Experimental results show that the proposed approaches yield significant schedulability improvement for RT tasks and turnaround time decrement for BE tasks. Moreover, the analysis of two driving scenarios shows that such schedulability improvement and turnaround time decrement can significantly enhance the driving safety and experience. For example, when the resource-aware non-uniform slack distribution approach is used, the distance that a car travels during the time between a traffic sign (pedestrian) is "seen and recognized" is decreased from 44.4m to 22.2m (from 4.4m to 2.2m); when the deadline-aware dynamic GPU partitioning approach is used, the distance that the car has traveled before a drowsy driver is woken up is reduced from 56.2m to 29.2m.