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- W96008780 abstract "Overly optimistic processing in Time Warp can threaten the stability of the simulation due to large memory consumption and explosive rollback growth. To address the stability concerns of optimistic simulation, Choe and Tropper proposed a learning-based flow control algorithm which throttles over-optimistic execution by regulating the flow of events between pairs of processors throughout the course of the simulation. This flow control algorithm has been shown to effectively improve simulation stability for certain applications in a shared-memory environment. In this paper we present an analysis and experimental verification of the performance of this flow control algorithm in a distributed-memory environment. Results show that the flow control algorithm reduces the memory usage, the number of rollbacks and the number of antievents at the expense of the simulation time. Thus it becomes apparent that the behaviour of the flow control algorithm is not a consequence of learning, but it is highly dependent on the type of simulation platform, event granularity and communication latency. Taking these results into account, we discuss a number of approaches to learning and flow control using the outlines of the flow control algorithm, and we consider the extent of the performance improvement to be expected from memory-based schemes for limiting Time Warp optimism in a distributed-memory environment." @default.
- W96008780 created "2016-06-24" @default.
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- W96008780 date "2003-01-01" @default.
- W96008780 modified "2023-09-27" @default.
- W96008780 title "Flow Control in Optimistic Simulation" @default.
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