An analytic method for predicting simulation parallelism

Abstract

The ability to predict the performance of a simulation application before its implementation is an important factor for the adoption of parallel simulation technology in industry. Ideally, a simulationist estimates the inherent parallelism of a simulation problem to determine whether it is worthwhile to invest resources to carry out a parallel simulation. We propose an analytic method for predicting the simulation parallelism of a simulation problem that is independent of implementation details. We assume that the system to be simulated is modelled as a network of logical processes, and each logical process models a queuing server center. Unlike many analytic models reported in the literature, we consider the causal relations among events in a simulation. Causality effects reduce event parallelism. Our proposed analytic method gives a tighter upper bound on performance speedup. Validation experiments show that our analytic prediction of simulation parallelism differs from that of critical path analysis by 2.9% and 18.8% in open and closed systems respectively.