Breathing Time Warp

Steinman, Jeff S.

doi:10.1145/174134.158473

Cited by 61 publications

(67 citation statements)

References 4 publications

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“…Many other techniques for optimism control have been developed over the last two decades. Examples include the Breathing Time Warp protocol [89], the Global Progress Window algorithm [90], the Elastic Time algorithm [91], and the Switch Time Warp mechanism [92]. Likewise, varied flow-control and learning based algorithms have been defined (see, e.g., [93,94]).…”

Section: Optimism Control Mechanismsmentioning

confidence: 99%

“…If load migration is warranted, each pair of chosen nodes at the LP level then handles the actual load selection and transfer operations. With the Breathing Time Warp protocol [89], a parallel simulation is executed in a cyclic fashion. Each simulation cycle starts with a purely optimistic TW execution, but then switches to a risk-free synchronization mode using the Breathing Time Buckets algorithm [127].…”

Section: Dynamic Process Migrationmentioning

confidence: 99%

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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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Section: Optimism Control Mechanismsmentioning

confidence: 99%

Section: Dynamic Process Migrationmentioning

confidence: 99%

Synchronization methods in parallel and distributed discrete-event simulation

Jafer

Liu

Wainer

2013

Simulation Modelling Practice and Theory

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“…The exploitation of the event history joins this sparse state saving approach and several techniques introduced with the aim of limiting the optimism of Time Warp (for example those in [3,4,21]), with the difference that the reduction of the rollback overhead is achieved by reducing the rollback cost instead of the rollback frequency.…”

Section: *Work Partially Supported By Grant No Erb4050pl932483 From mentioning

confidence: 99%

“…The state saving scheme we propose is based on the belief that the likelihood of a rollback point being contained in the virtual time interval I, the delimiting points of which are timestamps of successive events, increases with the distance between those points (a similar observation has been made in [21], with the difference that the starting point of the interval is the GVT of the simulation).…”

Section: A Sparse State Saving Schemementioning

confidence: 99%

Event history based sparse state saving in time warp

Quaglia

1998

SIGSIM Simul. Dig.

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“…The method in general represents an optimistic synchronization with time windows (e.g. [6], [7]), and was adapted (by introducing simulation time steps) to specific of wireless communication with contention based media access, as in 802.11 based wireless networks…”

Section: Introductionmentioning

confidence: 99%

Evaluations of the New Synchronization Method for the Parallel Simulations of Wireless Networks

Nowak¹,

Debudaj-Grabysz²,

Nowak³

2013

IJCTE

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Abstract-The article describes the implementation of the new synchronization method for parallel discrete event simulation and presents the experimental results obtained from parallel environment. In the proposed method the time steps are introduced and the synchronization occurs only at the end of a time step to achieve reduction of messages, exchanged between local processes of simulation. The method was implemented in C++, using the MPI library. The studies have shown that it is possible, under certain conditions and using appropriate hardware architecture, the significant acceleration of distributed simulations.

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Breathing Time Warp

Cited by 61 publications

References 4 publications

Synchronization methods in parallel and distributed discrete-event simulation

Synchronization methods in parallel and distributed discrete-event simulation

Event history based sparse state saving in time warp

Evaluations of the New Synchronization Method for the Parallel Simulations of Wireless Networks

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