Efficient optimistic parallel simulations using reverse computation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Proceedings of the 21st International Workshop on Principles of Advanced and Distributed Simulation
A hybrid epidemic model: combining the advantages of agent-based and equation-based approaches
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
An application of parallel Monte Carlo modeling for real-time disease surveillance
Proceedings of the 40th Conference on Winter Simulation
Proceedings of the 23rd international conference on Supercomputing
Parallel simulation on supercomputers
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
Communications of the ACM
Proceedings of the 2013 ACM SIGSIM conference on Principles of advanced discrete simulation
Indemics: An interactive high-performance computing framework for data-intensive epidemic modeling
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on simulation in complex service systems
Designing an agent based model for the efficient removal of red imported fire ant colonies
Proceedings of the 2013 Summer Computer Simulation Conference
| Hi-index | 0.02 |
In complex phenomena such as epidemiological outbreaks, the intensity of inherent feedback effects and the significant role of transients in the dynamics make simulation the only effective method for proactive, reactive or post facto analysis. The spatial scale, runtime speed, and behavioral detail needed in detailed simulations of epidemic outbreaks cannot be supported by sequential or small-scale parallel execution, making it necessary to use large-scale parallel processing. Here, an optimistic parallel execution of a new discrete event formulation of a reaction-diffusion simulation model of epidemic propagation is presented to facilitate a dramatic increase in the fidelity and speed by which epidemiological simulations can be performed. Rollback support needed during optimistic parallel execution is achieved by combining reverse computation with a small amount of incremental state saving. Parallel speedup of over 5,500 and other runtime performance metrics of the system are observed with weak-scaling execution on a small (8,192-core) Blue Gene/P system, while scalability with a weak-scaling speedup of over 10,000 is demonstrated on 65,536 cores of a large Cray XT5 system. Scenarios representing large population sizes, with mobility and detailed state evolution modeled at the level of each individual, exceeding several hundreds of millions of individuals in the largest cases, are successfully exercised to verify model scalability.