A rollback operation in a speculative parallel discrete event simulator has traditionally targeted the perfect reconstruction of the state to be restored after a timestamp-order violation. This imposes that the rollback support entails specific capabilities and consequently pays given costs. In this article we propose approximated rollbacks, which allow a simulation object to perfectly realign its virtual time to the timestamp of the state to be restored, but lead the reconstructed state to be an approximation of what it should really be. The advantage is an important reduction of the cost for managing the state restore task in a rollback phase, as well as for managing the activities (i.e. state saving) that actually enable rollbacks to be executed. Our proposal is suited for stochastic simulations, and explores a tradeoff between the statistical representativeness of the outcome of the simulation run and the execution performance. We provide mechanisms that enable the application programmer to control this tradeoff, as well as simulation-platform level mechanisms that constitute the basis for managing approximate rollbacks in general simulation scenarios. A study on the aforementioned tradeoff is also presented.
2020, Proceedings of the 2020 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, Pages 23-33
Approximated Rollbacks (04b Atto di convegno in volume)
Principe Matteo, Piccione Andrea, Pellegrini Alessandro, Quaglia Francesco