ACM Transactions on Programming Languages and Systems (TOPLAS)
Distributed discrete-event simulation
ACM Computing Surveys (CSUR)
PADS '93 Proceedings of the seventh workshop on Parallel and distributed simulation
Time, clocks, and the ordering of events in a distributed system
Communications of the ACM
Stochastic reaction-diffusion simulation with MesoRD
Bioinformatics
A spatial model of the red queen effect
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Distributed Simulation: A Case Study in Design and Verification of Distributed Programs
IEEE Transactions on Software Engineering
A parallel and distributed discrete event approach for spatial cell-biological simulations
ACM SIGMETRICS Performance Evaluation Review
On Parallel Stochastic Simulation of Diffusive Systems
CMSB '08 Proceedings of the 6th International Conference on Computational Methods in Systems Biology
A flexible and scalable experimentation layer
Proceedings of the 40th Conference on Winter Simulation
Simulation of Stochastic Reaction-Diffusion Processes on Unstructured Meshes
SIAM Journal on Scientific Computing
HIBI '09 Proceedings of the 2009 International Workshop on High Performance Computational Systems Biology
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Abstract: The spatial stochastic simulation of biochemical systems requires significant calculation efforts. Parallel discrete-event simulation is a promising approach to accelerate the execution of simulation runs. However, achievable speedup depends on the parallelism inherent in the model. One of our goals is to explore this degree of parallelism in the Next Subvolume Method type simulations. Therefore we introduce the Abstract Next Subvolume Method, in which we decouple the model representation from the sequential simulation algorithms, and prove that state trajectories generated by its executions statistically accord with those generated by the Next Subvolume Method. The experimental performance analysis shows that optimistic synchronization algorithms, together with careful controls over the speculative execution, are necessary to achieve considerable speedup and scalability in parallel spatial stochastic simulation of chemical reactions. Our proposed method facilitates a flexible incorporation of different synchronization algorithms, and can be used to select the proper synchronization algorithm to achieve the efficient parallel simulation of chemical reactions.