SHAPE: Scheduling of Fixed-Priority Tasks on Heterogeneous Architectures with Multiple CPUs and Many PEs

Yuankai Xu, Tiancheng He, Ruiqi Sun, Yier Jin, Yehan Ma, An Zou

Abstract:

Despite being employed in burgeoning efforts to accelerate artificial intelligence, heterogeneous architectures have yet to be well managed with strict timing constraints. As a classic task model, multi-segment self-suspension (MSSS) has been proposed for general I/O-intensive systems and computation offloading. However, directly applying this model to heterogeneous architectures with multiple CPUs and many processing units (PEs) suffers tremendous pessimism.
In this paper, we present a real-time scheduling approach, SHAPE, for general heterogeneous architectures with significant schedulability and improved utilization rate.
We start with building the general task execution pattern on a heterogeneous architecture integrating multiple CPU cores and many PEs such as GPU
streaming multiprocessors and FPGA IP cores. A real-time scheduling strategy and corresponding schedulability analysis are presented following the task execution pattern.
Compared with state-of-the-art scheduling algorithms through comprehensive experiments on unified and versatile tasks, SHAPE improves the schedulability by 11.1% - 100%. Moreover, experiments performed on the NVIDIA GPU systems further indicate up to 70.9% of pessimism reduction can be achieved by the proposed scheduling. Since we target general heterogeneous architectures, SHAPE can be directly applied to off-the-shelf heterogeneous computing systems with guaranteed deadlines and improved schedulability.