Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Proceedings of the 23rd international conference on Supercomputing
Switching to High Gear: Opportunities for Grand-Scale Real-Time Parallel Simulations
DS-RT '09 Proceedings of the 2009 13th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications
Efficient simulation of agent-based models on multi-GPU and multi-core clusters
Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques
Winter Simulation Conference
Reversible Parallel Discrete-Event Execution of Large-Scale Epidemic Outbreak Models
PADS '10 Proceedings of the 2010 IEEE Workshop on Principles of Advanced and Distributed Simulation
Agent-based simulation for large-scale emergency response: A survey of usage and implementation
ACM Computing Surveys (CSUR)
DS-RT '12 Proceedings of the 2012 IEEE/ACM 16th International Symposium on Distributed Simulation and Real Time Applications
Efficient implementation of complex interventions in large scale epidemic simulations
Proceedings of the Winter Simulation Conference
Interaction-based HPC modeling of social, biological, and economic contagions over large networks
Proceedings of the Winter Simulation Conference
Hierarchical multi-agent-based model for simulating the prevalence and evolution of influenza virus
Proceedings of the Agent-Directed Simulation Symposium
Designing an agent based model for the efficient removal of red imported fire ant colonies
Proceedings of the 2013 Summer Computer Simulation Conference
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We describe a distributed agent based epidemic model that is capable of easily simulating several hundred million agents. The model is adaptable to shared-memory and distributed-memory architectures. Several problems are addressed to enable the distributed simulation: allocation of agents to available compute nodes, periodic synchronization of compute nodes, and efficient communication between compute nodes. We assert that our modeling scheme is easily adaptable to different hardware environments and does not require large investments in performance tuning or special case coding.