Agent Based Modelling (ABM) systems have become a popular technique for describing complex and dynamic systems. ABM is the simulation of intelligent agents and how these agents communicate with each other within the model. The growing number of agent-based applications in the simulation and AI fields led to an increase in the number of studies that focused on evaluating modelling capabilities of these applications. Observing system performance and how applications behave during increases in population size is the main factor for benchmarking in most of these studies. System scalability is not the only issue that may affect the overall performance, but there are some issues that need to be dealt with to create a standard benchmark model that meets all ABM criteria. This paper presents a new benchmark model and benchmarks the performance characteristics of the FLAME GPU simulator as an example of a parallel framework for ABM. The aim of this model is to provide parameters to easily measure the following elements: system scalability, system homogeneity, and the ability to handle increases in the level of agent communications and model complexity. Results show that FLAME GPU demonstrates near linear scalability when increasing population size and when reducing homogeneity. The benchmark also shows a negative correlation between increasing the communication complexity between agents and execution time. The results create a baseline for improving the performance of FLAME GPU and allow the simulator to be contrasted with other multi-agent simulators.

GPUs have been demonstrated to be highly effective at improving the performance of Multi-Agent Systems (MAS). One of the major limitations of further performance improvements is in the memory bandwidth required to move agent data through the GPU's memory hierarchy. This paper presents a formal model for data aware simulation and an empirical study into the impact of minimising data movement on performance. This study proposes a method that can be applied to the simulation of complex systems on GPUs to extract required data from agent behaviour during simulation time and how this information can be used to reduce data movement. The FLAME GPU software has been extended to demonstrate this technique. Three benchmark experiments have been applied to evaluate the overall reduction in simulation execution time under specific criteria. The results of the comparison between the current and new system show that reducing data movement within a simulation improves overall performance with up to 4.8x speedup reported.