Abstract
Agent-based models (ABMs) need to populate a mega number of agents over a scalable simulation space in order to handle practical problems, (e.g., metropolitan traffic simulation and nationwide epidemic prediction). Although parallel and distributed simulation have steadily addressed their computational needs, non-computing scientists still tend to use GUI-rich, easy-to-use ABM interpretive platforms. This paper intends to identify the difficulty in using the current parallel ABM simulators and to propose their future improvements. For this purpose, we surveyed different ABM applications, modeled them as seven benchmark test cases, used them to analyze the agent descriptivity of parallel ABM simulators, and evaluated their execution performance affected by the current implementations.
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