Exploiting Dataflow Models for Parallel Simulation of Discrete Timed Systems

The shift towards parallel computing witnessed since the turn of this century has forced us to rethink traditional software design paradigms to better utilize resources. Yet, the simulation of time-aware systems remains a challenging topic due to the inherent semantics of time and causality whose consistency needs to be controlled, traditionally in form of a global event queue, limiting the potential for parallel exploitation. We propose a rehash of this problem by tackling it from a different modeling perspective, one which is able to express concurrency more naturally, i.e. dataflow (DF) models of computation (MoCs). By abstracting time aspects as an algebra hosted on a pure DF MoC, we are able to apply recent results from MoC theory not only for the purpose of describing deterministic behaviors for distributed timed systems, but also to overcome the existing limitations of timed execution in order to increase a simulation model's performance. We use a well-known example of a deadlock-prone distributed discrete event system as a driver to introduce the modeling concepts and show their potential for parallelism.