Multiprocessor Scheduling of an SDF Graph with Library Tasks Considering the Worst Case Contention Delay

Abstract

Recently a novel extension of a dataflow model with a library task has been proposed to overcome the severe limitation of dataflow models to handle shared resources. The library task that contains library functions and shared data inside plays the role of a server task when dataflow tasks as clients call library functions. In this paper, we propose a meta-heuristic technique based on a multi-objective genetic algorithm to find Pareto-optimal solutions in terms of resource requirement and the worst-case response time (WCRT) of the extended synchronous dataflow (SDF) graph with library tasks. For a given task graph, the proposed technique determines not only the mapping and scheduling in a heterogeneous multiprocessor system, but also task priorities and library task duplication. When multiple tasks request the service of the library task simultaneously, a task may experience a significant contention delay. For fast design space exploration, a fast and conservative method to estimate the contention delay of library tasks is devised. With synthetic examples and two real-life applications, the viability of the proposed technique is verified.