Event-driven delay-induced tasks: Model, analysis, and applications

Parallel execution and hardware acceleration involving specialized devices such as GPUs and FPGAs are becoming increasingly relevant in the domain of embedded systems. Communication between jobs dispatched on different cores and hardware accelerators is most often implemented using asynchronous events. Modeling the timing behavior of such systems requires to account for the delays incurred by each task due to the additional time spent waiting for events. This paper presents the event-driven delay-induced (EDD) task model to explicitly deal with complex computing workloads that incur such kinds of delays. The EDD task model generalizes several state-of-the-art models, such as the DAG task model and the segmented self-suspending task model, and is particularly suited to analyze parallel tasks that issue asynchronous hardware acceleration requests. Two analysis techniques for EDD tasks executing on single core platforms are first provided. We then extend those approaches to analyze parallel real-time tasks under partitioned multicore scheduling by means of a model transformation. Experimental results are presented to compare the two analysis techniques for EDD tasks proposed in the paper. Finally, we compare the analysis of partitioned parallel tasks modeled with EDD tasks against federated scheduling.