Shared memory parallel regenerative queuing network simulation

Discrete-event stochastic simulation is one of the most commonly used tools for performance modeling and evaluation. Parallel/distributed simulation enables a simulation program to execute on a computing system containing multiple processors and aims in reducing the model's execution time. Three basic types of execution mechanisms have appeared. The first two (the conservative and the optimistic approach) aim in partitioning the simulation model into a number of sub-models, also called logical processes (LPs). Their emphasis, lies on the specification of the appropriate synchronization, deadlock handling and/or memory management algorithms. The third approach (known as the time parallel approach or simply as Multiple Replications in Parallel Time Streams), aims in overcoming the need for sufficiently long runs in steady-state stochastic simulations, by executing multiple replications of the entire model in a parallel fashion. This work, presents a fast parallel OpenMP based implementation, for multivariate queuing network simulations. The simulation results are statistically processed, by applying the classical regenerative method under the Lavenberg & Sauer sequential analysis procedure. The first experimental results indicate significant speedups accompanied by acceptable confidence interval coverage.