Aurora: An Approach to High Throughput Parallel Simulation

Park, Alfred; Fujimoto, Richard M.

doi:10.1109/pads.2006.11

Cited by 16 publications

(10 citation statements)

References 18 publications

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“…The advantages of such approach over conventional PDES include support for execution over heterogeneous distributed computing platforms, load balancing, efficient execution on shared platforms, easy addition or removal of client machines during execution, simpler fault tolerance, and improved portability. The Aurora Parallel and Distributed Simulation System (Aurora) [135] is a prototype implementation of the master/worker paradigm. Several extensions and improvements to Aurora were presented later on including a scalable version for parallel discrete event simulations on desktop girds [136], an optimistic time management compliant for publicresource computing infrastructures and desktop grids [137], and a version implemented for metacomputing systems [138].…”

Section: Parallel and Distributed Environmentsmentioning

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: Parallel and Distributed Environmentsmentioning

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 MW paradigm offers several important advantages over traditional PDES implementations such as transparent fault tolerance, support for dynamic load balancing, and execution on shared resources [3]. The MW paradigm provides automated system level support for many tasks, greatly simplifying exploitation of parallel execution by PDES users.…”

Section: Background and Motivationmentioning

confidence: 99%

“…Previous work on the Aurora system has focused on providing a middleware system that is portable across varying platforms through web services [3] and accommodating multiple simultaneous large scale PDES applications using conservative synchronization [4]. The work presented in this paper describes the issues raised and possible solution approaches in applying the MW computing paradigm for optimistic time management.…”

Section: Introductionmentioning

confidence: 99%

Optimistic Parallel Simulation over Public Resource-Computing Infrastructures and Desktop Grids

Park

Fujimoto

2008

2008 12th IEEE/ACM International Symposium on Distributed Simulation and Real-Time Applications

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Issues concerning optimistic time management on public-resource computing infrastructures and desktop grids are explored. The master/worker (MW) approach used for these platforms calls for a rethinking of optimistic synchronization and the development of new mechanisms and protocols specific to this paradigm. Approaches to rollback, message cancellation, state management and state saving are described.Challenges specific to adapting optimism to this computing paradigm are discussed as well as optimizations and overhead reduction techniques. The impact of various key parallel discrete event simulation (PDES) application properties on performance of an optimistic MW implementation is evaluated using Aurora, a framework supporting PDES over desktop grids.

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“…Note that if the federation has components that cross firewalls, only the pull model can be used; network callbacks are rarely allowed through firewalls. At Georgia Tech, the IDSim project has recently morphed into a prototype system called Aurora [11], which is focused on running large distributed simulations in a grid computing environment.…”

Section: Idsimmentioning

confidence: 99%

Smallpox over San Diego: Joint Real-Time Federations of Distributed Simulations and Simulation Users under a Common Scenario

Linebarger

Goldsby

Fellig

et al. 2007

21st International Workshop on Principles of Advanced and Distributed Simulation (PADS'07)

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A joint project between the California and New Mexico branches of Sandia National Laboratories has demonstrated the formation of joint real-time federations of both distributed simulations and distributed simulation users under a common scenario.Two software integration frameworks were used to achieve the realtime federations. The IDSim framework, developed by Georgia Tech University and Sandia National Laboratories, was used to create the real-time federation of distributed simulations, in this case the BioDAC WMD simulation and the N-ABLE™ agent-based microeconomic simulation (more properly, because of the impact of hurricanes Katrina and Rita, an N-ABLE™ emulator). The GroupMeld™ multimedia synchronous collaboration framework, developed by Sandia, was used to create the real-time federation of simulation users and simulation analysis communities. The common scenario was the release of smallpox over San Diego, California, and the operating hypothesis was that the economy itself dampens the spread of a pathogen. In addition, a small pilot experiment using the joint federations allowed a greater range of crisis management options to be performed and evaluated than would have been possible without the use of the integration frameworks.

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Aurora: An Approach to High Throughput Parallel Simulation

Cited by 16 publications

References 18 publications

Synchronization methods in parallel and distributed discrete-event simulation

Synchronization methods in parallel and distributed discrete-event simulation

Optimistic Parallel Simulation over Public Resource-Computing Infrastructures and Desktop Grids

Smallpox over San Diego: Joint Real-Time Federations of Distributed Simulations and Simulation Users under a Common Scenario

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