The performance of a distributed combat simulation with the time warp operating system

Wieland, Frederick; Hawley, L. R.; Feinberg, A.; Loreto, Mike Di; Blume, L.; Ruffles, Joseph; Reiher, Peter; Beckman, Brian; Hontalas, Philip; Bellenot, Steven F.; Jefferson, David

doi:10.1002/cpe.4330010105

Cited by 23 publications

(5 citation statements)

References 6 publications

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“…Time Warp is a relatively complex simulation protocol but it has been proved a very effective technique for running complex asynchronous simulations [23,24]. We foresee that with an implementation in Java the use of Time Warp could become more widespread within the research community and it can be used for educational purposes.…”

Section: Discussionmentioning

confidence: 99%

JWarp: a Java library for parallel discrete-event simulations

Bizarro¹,

Silva²,

Silva³

1998

Concurrency: Pract. Exper.

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Java is a very promising language for use in the simulation of physical models due to its objectoriented nature, portability, robustness and support for multithreading. This paper presents JWarp, a Java library for discrete-event parallel simulations. It is based on an optimistic model for synchronization of the simulation entities: the Time Warp mechanism. We introduce the main features of the library and discuss some of the implementation details.

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Section: Discussionmentioning

confidence: 99%

JWarp: a Java library for parallel discrete-event simulations

Bizarro¹,

Silva²,

Silva³

1998

Concurrency: Pract. Exper.

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“…The TW protocol has been employed in many real-world applications, achieving significant speedups in simulations of communication networks [67], battlefield scenarios [68], biological phenomena [69], and computer systems [70].…”

Section: Optimistic Synchronization Algorithmsmentioning

confidence: 99%

Synchronization methods in parallel and distributed discrete-event simulation

Jafer

Liu

Wainer

2013

Simulation Modelling Practice and Theory

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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

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“…array or hypercube and therefore the restrictions on model and size are eliminated: DDES can be used to simulate anything from military combat scenarios [8] to general queuing networks [9] and the problem size is no more restricted than for traditional software-based approaches.…”

Section: Introductionmentioning

confidence: 99%

“…The theory of distributed discrete-event simulation @DES)[4-71 provides a much more general method of distributing simulations over multiple processors. The target platform in this case is typically a loosely coupled MIMD system such as a transputer array or hypercube and therefore the restrictions on model and size are eliminated: DDES can be used to simulate anything from military combat scenarios [8] to general queuing networks [9] and the problem size is no more restricted than for traditional software-based approaches.…”

Section: Introductionmentioning

confidence: 99%

Distributing gate‐level digital timing simulation over arrays of transputers

Wood

1991

Concurrency: Pract. Exper.

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SUMMARYContinuing advances in VLSI fabrkation technology are allowing circuit designs to become more and more complex and are thereby fuelling the need for ever-faster digital simulators. In this paper, we Investigate a multi-transputer-based method for speeding up gate-level digital timing simulation, the acknowledged 'workhorse' of the digital circuit design verification process. In partkular, we describe a variant of the basic conservative method lor distributed discrete-event simulation and we present PARSIM, a gate-level digital timing simulator which is based on this method and which runs on arrays of transputers. Preliminary results on small transputer arrays demonstrate good speed-ups for bitske partitions of circuits with regular structure, including supralinear speed-ups for a large ( l e g a t e ) circuit. Although these results are encouraging, poor results lor less-than-ideal partitions of our test circuits suggest that we require an improvement in the efficiency of deadlock resolution and/or a means of automated optimized partitioning before PARSIM can be used as a practical tool for speeding up gate-level simulation.

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The performance of a distributed combat simulation with the time warp operating system

Cited by 23 publications

References 6 publications

JWarp: a Java library for parallel discrete-event simulations

JWarp: a Java library for parallel discrete-event simulations

Synchronization methods in parallel and distributed discrete-event simulation

Distributing gate‐level digital timing simulation over arrays of transputers

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