Event reconstruction in time warp

Li, Lijun; Tropper, Carl

doi:10.1145/1013329.1013337

Cited by 11 publications

(5 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In a more recent study, Li and Tropper argued that the event reconstruction technique, originally intended for memory conservation in Clustered Time Warp, could also be used to facilitate process migration because only the state data associated with a TWLP need to be transferred [85]. Nonetheless, the study did not indicate whether the proposed migration scheme would be realized at the cluster level or at the individual LP level.…”

Section: Dynamic Process Migrationmentioning

confidence: 99%

“…The event reconstruction technique proposed by Li and Tropper [85] intends to reduce the overhead associated with event saving. Based on the observation that the size of events can be very large in some cases, they suggested a method for reconstruction of both input and output events by comparing the differences between adjacent states saved in the state queues.…”

Section: Other Memory Conserving Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Synchronization methods in parallel and distributed discrete-event simulation

Jafer

Liu

Wainer

2013

Simulation Modelling Practice and Theory

View full text Add to dashboard Cite

a b s t r a c tThis work attempts to provide insight into the problem of executing discrete event simulation in a distributed fashion. The article serves as the state of the art in Parallel DiscreteEvent Simulation (PDES) by surveying existing algorithms and analyzing the merits and drawbacks of various techniques. We discuss the main characteristics of existing synchronization methods for parallel and distributed discrete event simulation. The two major categories of synchronization protocols, namely conservative and optimistic, are introduced and various approaches within each category are presented. We also present the latest efforts towards PDES on emerging platforms such as heterogeneous multicore processors, Web services, as well as Grid and Cloud environment.Ó 2012 Elsevier B.V. All rights reserved. Parallel discrete-event simulationWith the computing power and advanced software available today, modeling and simulation has become a cost-effective tool for detailed analysis of a broad array of natural and artificial systems. Parallel and distributed simulation is widely accepted as the technology of choice to speed up large-scale discrete-event simulation and to promote reusability and interoperability of simulation components. Parallel Discrete-Event Simulation (PDES) has received increasing interest as simulations become more time consuming and geographically distributed. A rich literature has already been developed in the last three decades, taking advantage of the increasing availability of highly parallel and distributed computing platforms. It has been several years since the last survey by Perumalla [5] and a refreshed review of the latest developments would be needed for us to ponder new research initiatives, especially on emerging platforms such as many-core processors, Internetscale simulation environments, and cloud-based virtualized infrastructures.In parallel and distributed simulations, the entire simulation task is divided into a set of smaller subtasks with each executed on a different processor or node. Hence, the simulation system is viewed as a collection of concurrent processes, each modeling a different part of the physical system and executing on a dedicated processor in a sequential fashion. These processes communicate with each other by exchanging time-stamped event messages. An event refers to an update to simulation system state at a specific simulation time instant. Throughout the simulation, events arrive at destination processes, and depending on the delivery ordering system of the simulation, they are processed differently. The two commonly used orderings are (1) receive-order and (2) timestamp-order. With the first type, events are delivered to the destination processes when they arrive at the destination. On the other hand, with the timestamp-order, events are delivered in non-decreasing order of their timestamp, requiring runtime checks and buffering to ensure such ordering.1569-190X/$ -see front matter Ó

show abstract

Section: Dynamic Process Migrationmentioning

confidence: 99%

Section: Other Memory Conserving Techniquesmentioning

confidence: 99%

Synchronization methods in parallel and distributed discrete-event simulation

Jafer

Liu

Wainer

2013

Simulation Modelling Practice and Theory

View full text Add to dashboard Cite

show abstract

“…Several reverse computation models have been designed for a variety of models, including communications network modeling , Bauer et al 2006, and physical systems (Tang et al 2006). Additional optimizations for reducing memory consumption related to the storing of processed events was proposed in Bauer and Page (2007) for reverse computation and Li and Tropper (2004) for state-saving.…”

Section: Related Workmentioning

confidence: 99%

Optimistic parallel discrete event simulation of the event-based transmission line matrix method

Bauer¹

2007

2007 Winter Simulation Conference

View full text Add to dashboard Cite

In this paper we describe a technique for efficient parallelization of digital wave guide network (DWN) models based on an interpretation of the finite difference time domain (FDTD) method for discrete event simulation. Modeling methodologies based on FDTD approaches are typically constrained in both the spatial and time domains. This interpretation for discrete event simulation allows us to investigate the performance of DWN models in the context of optimistic parallel discrete event simulation employing reverse computation for rollback support. We present parallel performance results for a large-scale simulation of a 3D battlefield scenario, 100km 2 and at a height of 100m with a resolution of 100m in the X-, Y-planes, and 10m in the Z-plane for 754 simultaneous radio wave transmissions.

show abstract

“…The primary implementation for rollback relies on saving the state of the model such that the entities, or logical processes (LPs) can be restored when errors in the causality constraint occur (Lamport 1978). A great deal of research has been performed investigating optimizations for state-saving (West 1988, Lin et al 1993, Steinman 1993, Fleischmann and Wilsey 1995, Gomes 1996, Quaglia 1999, Chen and Szymanski 2003, Li and Tropper 2004, Zeng et al 2004.…”

Section: Introductionmentioning

confidence: 99%

Scalable RF propagation modeling on the IBM Blue Gene/L and Cray XT5 supercomputers

Bauer

Carothers

2009

Proceedings of the 2009 Winter Simulation Conference (WSC)

View full text Add to dashboard Cite

We present a performance analysis for a highly accurate, large-scale electromagnetic wave propagation model on two modern supercomputing platforms: the Cray XT5 and the IBM Blue Gene/L. The electromagnetic wave model is used to simulate the physical layer of a large-scale mobile ad-hoc network of radio devices. The model is based on the numerical technique called Transmission Line Matrix, and is implemented in a Time Warp simulation package that employs reverse computation for the rollback mechanism. Using Rensselaer's Optimistic Simulation System we demonstrate better than real-time, scalable parallel performance for network scenarios containing up to one million mobile radio devices, highly accurate RF propagation and high resolution, large-scale complex terrain.

show abstract

Event reconstruction in time warp

Cited by 11 publications

References 13 publications

Synchronization methods in parallel and distributed discrete-event simulation

Synchronization methods in parallel and distributed discrete-event simulation

Optimistic parallel discrete event simulation of the event-based transmission line matrix method

Scalable RF propagation modeling on the IBM Blue Gene/L and Cray XT5 supercomputers

Contact Info

Product

Resources

About