Distributed agent-based traffic simulations

Modeling and simulation play an important role in transportation networks analysis. With the widespread of personalized real-time information sources, it is relevant for the simulation model to be individual-centered. The agent-based simulation is the most promising paradigm in this context. However, representing the movements of realistic numbers of travelers within reasonable execution times requires significant computational resources. It also requires relevant methods, architectures and algorithms that respect the characteristics of transportation networks. In this paper, we tackle the problem of using high-performance computing for agent-based traffic simulations. To do so, we define two generic agent-based simulation models, representing the existing sequential agent-based traffic simulations. The first model is macroscopic, in which travelers do not interact directly and use a fundamental diagram of traffic flow to continuously compute their speeds. The second model is microscopic, in which travelers interact with their neighbors to adapt their speeds to their surrounding environment. We define patterns to distribute these simulations in a high-performance environment. The first distributes agents equally between available computation units. The second pattern splits the environment over the different units. We finally propose a diffusive method to dynamically balance the load between units during execution. The results show that agent-based distribution is more efficient with macroscopic simulations, with a speedup of 6 compared to the sequential version, while environmentbased distribution is more efficient with microscopic simulations, with a speedup of 14. Our diffusive load-balancing algorithm improves further the performance of the environment based approach by 150%

Effective Distribution of Large Scale Situated Agent-based Simulations

International audienceAgent-based simulations have increasing needs in computational and memory resources when the the number of agents and interactions grows. In this paper, we are concerned with the simulation of large scale situated multi-agent systems (MAS). To be able to simulate several thousands or even a million of agents, it becomes necessary to distribute the load on a computer network. This distribution can be done in several ways and this paper presents two specific distributions: the first one is based on environment and the second one is based on agents. We illustrates the pros and cons of using both distribution types with two classical MAS applications: prey-predator and flocking behaviour models