Conservative synchronization of large-scale network simulations

Park, Alfred; Fujimoto, Richard M.; Perumalla, Kalyan S.

doi:10.1145/1013329.1013355

Cited by 27 publications

(16 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The performance of a CEL-based parallel discrete-event simulator was studied using a benchmark called DEVStone [187] that generates a suite of models with varied structure and behavior automatically. Park et al [39] compared the performance and scalability of a lazy null message algorithm with global reduction approaches. They suggested that, for scenarios simulating scaled network models with constant number of input and output channels per LP, the lazy null message algorithm offers better scalability than efficient global reduction based synchronous protocols.…”

Section: Variations Of Cmbmentioning

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

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: Variations Of Cmbmentioning

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

“…Some recent advancements and interesting results in the optimistic algorithms can be found in [7][8][9][10]. Conservative protocols fundamentally maintain causality in event execution by strictly disallowing the processing of events out of time-stamp order [4]. Some recent research on conservative algorithms in DES can be found in [11][12][13][14].…”

Section: Event Synchronization Through Conservative Tmamentioning

confidence: 99%

“…In conservative distributed simulation, event-messages are required to execute by LPs such that local causality constraint requirement must not be violated [4]. To do that, each LP must know which event-messages are safe to process.…”

Section: Introductionmentioning

confidence: 99%

A Logical Process Simulation Model for Conservative Distributed Simulation Systems

Rizvi¹

2013

Int. j. simul. model.

View full text Add to dashboard Cite

This paper presents a new logical process (LP) simulation model for distributed simulation systems where Null Message Algorithm (NMA) is used as an underlying time management algorithm (TMA) to provide synchronization among LPs. To extend the proposed simulation model for n number of LPs, this paper provides a detailed overview of the internal architecture of each LP and its coordination with the other LPs through sub-system components and models such as communication interface and simulation executive. The proposed architecture of LP simulation model describes the proper sequence of coordination that need to be done among LPs though different subsystem components and models to achieve synchronization. To execute the proposed LP simulation model for different set of parameters, a queuing network model is used. Experiments will be performed to verify the accuracy of the proposed simulation model using the pre-derived mathematical equations. Our numerical and simulation results can be used to observe the exchange of null messages and overhead indices.

show abstract

“…Their combined performance can be expected to be better than each of them running individually and separately. Our current implementation includes two different modules: one is based on efficient global hierarchical reductions [20,22], while the other is based on an optimized variant [23] of the Chandy-Misra-Bryant null message algorithm [24]. These have been tested on large-scale platforms, and have been demonstrated to scale very well, even up to supercomputing configurations of more than 1500 processors [22,23,25].…”

Section: Distributed Time Synchronizationmentioning

confidence: 99%

µsik — A Micro-Kernel for Parallel/Distributed Simulation Systems

Perumalla

Workshop on Principles of Advanced and Distributed Simulation (PADS'05)

View full text Add to dashboard Cite

show abstract

Conservative synchronization of large-scale network simulations

Cited by 27 publications

References 29 publications

Synchronization methods in parallel and distributed discrete-event simulation

Synchronization methods in parallel and distributed discrete-event simulation

A Logical Process Simulation Model for Conservative Distributed Simulation Systems

µsik — A Micro-Kernel for Parallel/Distributed Simulation Systems

Contact Info

Product

Resources

About