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

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.