Scheduling DAGs of Multi-Version Multi-Phase Tasks on Heterogeneous Real-Time Systems

Heterogeneous high performance embedded systems are increasingly used in industry. Nowadays, these platforms embed accelerator-style components, such as GPUs, alongside different CPU cores. We use multiple alternatives/versions/implementations of tasks to fully benefit from the heterogeneous capacities of such platforms and due to binary incompatibility. Implementations targeting accelerators not only require access to the accelerator but also to a CPU core for, e.g., pre-processing and branching the control flow. Hence, accelerator workloads can naturally be divided into multiple phases (e.g. CPU, GPU, CPU). We propose an asynchronous scheduling approach that utilises multiple phases and thereby enables a finegrained scheduling of tasks that require two types of hardware. We show that our approach can increase the schedulability rate by up 24% over two multi-version phase-unaware schedulers. Additionally, we demonstrate that the schedulability rate of our heuristic is close to the optimal schedulability rate.