Adaptive algorithms for the dynamic distribution and parallel execution of agent-based models

We propose a framework for defining agent-based models (ABMs) and two algorithms for the automatic parallelization of agent-based models, a general version P-ABMG for all ABMs definable in the framework and a more specific variant P-ABMS for "spatial ABMs" targeted at SWARM and ANT-based models, where the additional spatial information can be utilized to obtain performance improvements. Both algorithms can automatically distribute ABMs over multiple CPUs and dynamically adjust the degree of parallelization based on available computational resources throughout the simulation runs. We also describe a first implementation of P-ABMS in our SWAGES environment and report both results from simulations with simple SWARM agents that provide a lower bound for the performance gains achievable by the algorithm and results from simulations with more complex deliberative agents, which need to synchronize their state after each update cycle. Even in the latter case, we show that in some conditions the algorithm is able to achieve close-to-maximum performance gains.