Parallel discrete event simulation

Fujimoto, Richard M.

doi:10.1145/76738.76741

Cited by 191 publications

(246 citation statements)

References 24 publications

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“…With such information, each LP can safely process any pending events with a timestamp smaller than the LBTS value [4,53]. One of the challenges of time synchronization protocols is the transient message problem [2]. A transient message is a message that has been sent but not yet received by the intended recipient.…”

Section: Deadlock Avoidancementioning

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: Deadlock Avoidancementioning

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 problem of determining the interest set for timestamp values in Stampede has similarity to the garbage collection problem in Parallel Discrete Event Simulation (PDES) systems [3]. However, the application model that Stampede run-time system supports is less restrictive.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Stampede Garbage Collection Algorithms

Mandviwala

Harel

Knobe

et al. 2005

Languages and Compilers for Parallel Computing

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Stampede is a parallel programming system to support interactive multimedia applications. The system maintains temporal causality in such streaming real-time applications via channels that buffer timestamped items. A Stampede application is a coarse-grain dataflow pipeline of these timestamped items. Not all timestamps are relevant for an application output due to the differential processing rates of pipeline stages. Therefore, garbage collection (GC) is crucial for Stampede runtime performance. Three GC algorithms are currently available in Stampede. In this paper, we ask the question how far off these algorithms are from an ideal garbage collector, one in which the memory usage is exactly equal to that which is required for buffering only the relevant timestamped items in channels. This oracle, while unimplementable, serves as an empirical lower-bound for memory usage. We then propose optimizations that will help us get closer to this lower-bound. Using an elaborate measurement and post-mortem analysis infrastructure in Stampede, we evaluate the performance potential for these optimizations. A color-based people tracking application is used for the performance evaluation. Our results show that these optimizations reduce the memory usage by over 60% for this application over the best GC algorithm available in Stampede.

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“…The LPGs are executed concurrently on the computing nodes involved in the distributed simulation. In TWLinuX, each LPG is implemented as one Linux process and each LP in an LPG is implemented as a separate Linux thread 1 . In this section, we provide an overview of the functionality that we have added to Linux in order to support PDES.…”

Section: Twlinux Implementationmentioning

confidence: 99%

“…The processing of an event may generate one or more future events. The objective of PDES is to reduce simulation time by parallelizing the simulation [1]. This is done by decomposing the sequential simulation model into a set of smaller sub-models, called logical processes or simulation processes, which can be executed concurrently on the different nodes of a distributed system, or in parallel on the nodes of a parallel system.…”

Section: Introductionmentioning

confidence: 99%

TWLinuX: Operating System Support for Optimistic Parallel Discrete Event Simulation

Sharma¹,

Thazhuthaveetil

2001

High Performance Computing — HiPC 2001

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Abstract. Parallel or Distributed Discrete Event Simulation (PDES) refers to the concurrent execution of a single discrete event simulation application on a parallel or distributed computing system. Most available PDES implementations provide user level library support for writing distributed simulation applications. We discuss how OS support can be designed to facilitate optimistic PDES of large, complex simulation models. TWLinuX is our implementation of these concepts through modification of Linux. Through TWLinuX, a simple, low cost network of machines becomes a high performance discrete event simulation platform.

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Parallel discrete event simulation

Cited by 191 publications

References 24 publications

Synchronization methods in parallel and distributed discrete-event simulation

Synchronization methods in parallel and distributed discrete-event simulation

A Comparative Study of Stampede Garbage Collection Algorithms

TWLinuX: Operating System Support for Optimistic Parallel Discrete Event Simulation

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