WARPED: a time warp simulation kernel for analysis and application development

Martin, Dale E.; McBrayer, T.J.; Wilsey, Philip A.

doi:10.1109/hicss.1996.495485

Cited by 59 publications

(44 citation statements)

References 2 publications

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“…There are several run-time support libraries for running parallel discrete-event simulations such as Root-Sim (Alessandro Pellegrini & Quaglia, 2014), ROSS (Carothers, Bauer, & Pearce, 2002), Warped (Martin, McBrayer, & Wilsey, 1996) or sik (Perumalla, 2005). As a case study, we chose sik simulation library for parallel discrete-event simulation (Onggo, 2008(Onggo, , 2010.…”

Section: A Solution For Parallel Demographic Simulation: Yades Frameworkmentioning

confidence: 99%

Approaching parallel computing to simulating population dynamics in demography

et al. 2016

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Agent-based modelling and simulation is a promising methodology that can be applied in the study of population dynamics. The main advantage of this technique is that it allows representing the particularities of the individuals that are modeled along with the interactions that take place among them and their environment. Hence, classical numerical simulation approaches are less adequate for reproducing complex dynamics. Nowadays, there is a rise of interest on using distributed computing to perform large-scale simulation of social systems. However, the inherent complexity of this type of applications is challenging and requires the study of possible solutions from the parallel computing perspective (e.g., how to deal with fine grain or irregular workload). In this paper, we discuss the particularities of simulating populating dynamics by using parallel discrete event simulation methodologies. To illustrate our approach, we present a possible solution to make transparent the use of parallel simulation for modeling demographic systems: Yades tool. In Yades, modelers can easily define models that describe different demographic processes with a web user interface and transparently run them on any computer architecture environment thanks to its demographic simulation library and code generator. Therefore, transparency is provided by by two means: the provision of a web user interface where modelers and policy makers can specify their agent-based models with the tools they are familiar with, and the automatic generation of the simulation code that can be executed in any platform (cluster or supercomputer). A study is conducted to evaluate the performance of our solution in a High Performance Computing environment. The main benefit of this outline is that our findings can be generalized to problems with similar characteristics to our demographic simulation model.

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Section: A Solution For Parallel Demographic Simulation: Yades Frameworkmentioning

confidence: 99%

Approaching parallel computing to simulating population dynamics in demography

et al. 2016

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“…Parallel discrete event simulators for fine-grained simulation, like Georgia Tech Time Warp (GTW) [7], Rensselaer's optimistic simulation system (ROSS) [6] or WARPED [24] do not employ IM. Instead, performance improvements are explored with various optimistic synchronisation techniques and, in case of GTW and WARPED, additionally with load distribution.…”

Section: Contrasting Related Workmentioning

confidence: 99%

Dynamically adaptive partition-based interest management in distributed simulation

Kumova

2006

Simulation Modelling Practice and Theory

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“…Let the total number of lost events at the merge point be denoted by G. We have G = X G i ; (for all routes in the fork-merge pair) (14) In the simulation of open systems, the population of simultaneous events is determined mainly by the event generators at steady state. Therefore the number of events lost in the merge points will not affect the workload characteristics.…”

Section: Figure 3 Fork and Merge Lp Networkmentioning

confidence: 99%

“…The two major approaches of asynchronous parallel simulation are conservative [4] and optimistic [11]. Synchronization protocols for parallel simulation have been proposed [4,11,16,18,19], and many experimental parallel simulation suites are available [2,6,14,20].…”

Section: Introductionmentioning

confidence: 99%