Clustered time warp and logic simulation

Avril, Hervé; Tropper, Carl

doi:10.1109/pads.1995.404310

Cited by 27 publications

(23 citation statements)

References 18 publications

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“…To enhance the performance of TW-based digital logic simulations, Avril and Tropper [107] introduced a Clustered Time Warp protocol that uses TW to synchronize clusters of LPs globally, whereas the execution of LPs in each cluster is scheduled sequentially by a cluster environment. The cluster environment uses a time zone table (to detect changes in virtual time), a cluster input queue (to receive events from other clusters), and a cluster output queue (to hold anti-messages that might be sent to other clusters during rollbacks).…”

Section: Lp Aggregation Techniquesmentioning

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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Section: Lp Aggregation Techniquesmentioning

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 major difference with Time Warp is that LPs can remove from their input queue all of the internal messages which have a send time greater than the timestamp of the straggler or the antimessage which caused the rollback. This does not affect the correctness of 1. A message which has a timestamp smaller then the timestamp of a message which has already been processed.…”

Section: Rolling Backmentioning

confidence: 99%

“…In our algorithms, we first group LPs into clusters [1], [2], associating an input queue and an output queue with each such cluster. The formation of clusters is determined by clustering algorithms which are application dependent.…”

Section: Introductionmentioning

confidence: 99%

On rolling back and checkpointing in time warp

Avril

Tropper

2001

IEEE Trans. Parallel Distrib. Syst.

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AbstractÐIn this paper, we present a family of three algorithms which serve to perform checkpoints and to roll back Time Warp. These algorithms are primarily intended for use in simulations in which there are a large number of LPs and in which events have a small computational granularity. Important representatives of this class are VLSI and computer network simulations. In each of our algorithms, LPs are gathered into clusters via algorithms which are application dependent. In order to examine the performance of our algorithms and to compare them to Time Warp, we made use of two of the largest digital logic circuits available from the ISCAS89 benchmark series of combinational circuits. The execution time, number of states saved, and maximal memory consumption were compared to the same quantities for Time Warp. Our results indicated that each of the algorithms occupies a different point in the spectrum of possible trade-offs between memory usage and execution time, ranging from substantial memory savings (at a comparable cost in speed) to memory savings and a comparable speed to Time Warp. Hence, an important benefit of our algorithms is the ability to trade off memory requirements with execution time.

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“…As in Ref. [2], an incremental state saving technique is under development which would improve roll back management and storage requirements. Simulation tuning is a key issue which deserves further investigation through a combination of analytical and experimental means.…”

Section: Conclusion and Further Workmentioning

confidence: 99%

“…Good speedups have been reported [S] in using TW to the simulation of complex systems like timed Petri nets [ 1,7] communication networks, queuing networks, logic simulation [2], combat models and so forth, on a variety of special purposes parallel architectures and of networked homogeneous systems [ 5,6].…”

Section: Introductionmentioning

confidence: 99%