The PDEVS formalism is widely used for the description and analysis of discrete event systems. But PDEVS has some drawbacks in modeling Mealy behavior. A revised version (RPDEVS) has been invented to resolve them, but it has problems of its own, mainly because its complicated simulator structure. The recently proposed NSA-DEVS scheme tries to unite the advantages of both formalisms by using infinitesimal time intervals.To further substantiate this claim we describe an abstract simulator for NSA-DEVS, implement it in Matlab and simulate a simple queue-server system. This shows that NSA-DEVS combines the Mealy-like model description of RPDEVS with the simple simulator structure of PDEVS, making it a promising approach to implement an improved modeling and simulation system.

To utilize the PDEVS formalism for the practical modeling and simulation of discrete-event systems, the recently proposed variant NSA-DEVS combines the Mealy behaviour of RPDEVS with a simple simulator algorithm by employing infinitesimal time delays. To further test the practical usefulness of this new approach, four simple systems showing non-trivial event-cascades are modeled and simulated within a concrete NSA-DEVS

The ARGESIM benchmark CP2 provides three different tasks to study current technologies for the parallelization of simulation programs. The first task is the Monte Carlo study. In this study, a spring-mass system is simulated with different damping factors. The second task is a Latice Boltzmann simulation in which the flow of a fluid in a special geomentry is simulated. The third problem is a partial differential equation (PDE) describing a swinging rope, which is solved by the Method of Lines. The Monte Carlo and the PDE study are solved here, each one with two different methods: The first one applies the standard MPI message passing library together with the GNU Scientific Library, the second one uses Matlab from The MathWorks in combination with the Parallel Computing Toolbox. A special focus of this work is on the parallel processing functions provided by Matlab. The solutions are compared with each other in terms of performance and scalability. In most cases, the solutions with OpenMPI and GSL were faster than the solutions with Matlab PCT. The Matlab PCT offers many functionalities and applications to accelerate, but these usually have a poor runtime behavior. SNE 32(4) -12/2022

The ARGESIM benchmark CP2 consists of three different tasks to study current technologies for the parallelization of simulation programs, two of which have been addressed in a previous publication. The third one is the study of the fluid flow in a special geometry using the Lattice Boltzmann method. The task is studied with two methods for up to 256 cores, again using the MPI message passing library Open MPI and Matlab from The MathWorks in combination with the Parallel Computing Toolbox. Solutions with different grid sizes are compared with each other in terms of runtime and speedup. The Open MPI version generally shows good speedups even for large core numbers, while the Matlab version has poor results for 32 cores or more. On the other hand, the scalar Matlab version is several times faster than the Open MPI version, leading to a smaller runtime for up to 32 cores.