Fast simulation, i.e., automatic computation of sequential runs, is widely used to analyse Petri nets. In particular, it enables for quantitative statistical analysis by observing large sets of runs. Moreover, fast simulation may be used to actually run a Petri net model as a (prototype) implementation of a system, in which case such a net would embed fragments of the code of the system. In both these contexts, being able to perform faster simulation is highly desirable. In this paper, we propose a way to accelerate fast simulation by exploiting parallel computing, targeting both the multi-core cpus available nowadays in every laptop or workstation, and larger parallel computers including those with distributed memory (clusters). We design an algorithm to do so and assess in particular its correctness and completeness through its formal modelling as a Petri net whose state space is analysed. We also present a benchmark of a prototype implementation that clearly shows how our algorithm effectively accelerates fast simulation, in particular in the case of large concurrent coloured Petri nets, which is precisely the kind of nets that are usually slow to simulate.

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Distributed storage systems are nowadays ubiquitous, often under the form of multiple caches forming a hierarchy. A large amount of work has been dedicated to design, implement and optimise such systems. However, there exists to the best of our knowledge no attempt to use formal modelling and analysis in this field. This paper proposes a formal modelling framework to design distributed storage systems, with the innovating feature to separate the various concerns they involve like data model, operations, placement, consistency, topology, etc. A system modelled in such a way can be analysed through model-checking to prove correctness properties, or through simulation to measure timed performance. In this paper, we define the modelling framework and then focus on timing analysis. We illustrate the latter aspect on a simple yet realistic example, showing that our proposal has the potential to be used to make design decisions before the real system is implemented.