Exploiting just-enough parallelism when mapping streaming applications in hard real-time systems

Embedded streaming applications specified using parallel Models of Computation (MoC) often contain ample amount of parallelism which can be exploited using Multi-Processor System-on-Chip (MPSoC) platforms. It has been shown that the various forms of parallelism in an application should be explored to achieve the maximum system performance. However, if more parallelism is revealed than needed, it will overload the underlying MPSoC platform. At the same time, the revealed parallelism should be sufficient such that the MPSoC platform is fully utilized. Therefore, the amount of revealed and exploited parallelism has to be just-enough with respect to the platform constraints. In this paper, we study the problem of exploiting just-enough parallelism by application task unfolding, when mapping streaming applications modeled using the Synchronous Data Flow (SDF) MoC onto MPSoC platforms in hard real-time systems. We show that our problem of simultaneously unfolding and allocating tasks under hard real-time scheduling has a bounded solution space and derive its upper bounds. Subsequently, we devise an efficient algorithm to solve the problem, while the obtained solution meets a pre-specified quality. The experiments on a set of real-life streaming applications demonstrate that our algorithm results, within reasonable amount of time, in a system specification with large performance gain. Finally, we show that our proposed algorithm is on average 100 times faster than one of the state-of-the-art meta-heuristics, i.e., NSGA-II genetic algorithm, while achieving the same quality of solutions.