Scheduling critical channels in conservative parallel discrete event simulation

Xiao, Zhonge; Unger, Brian; Simmonds, Rob; Cleary, John G.

doi:10.1109/pads.1999.766157

Cited by 52 publications

(28 citation statements)

References 9 publications

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“…The Critical Channel Traversing (CCT) algorithm [30] extended the CMB algorithm with the addition of rules that determine when to schedule an LP for event execution. CCT attempts to schedule the LPs with the largest number of events that are ready to execute.…”

Section: Variations Of Cmbmentioning

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: Variations Of Cmbmentioning

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 cores then communicate via exchanging messages through the shared memory. However, approaches to shared-memory PDES [8,9,24] have proved that it can be more efficient to conceptually design parallel simulation explicitly for multi-core machines rather than adopting distributed simulation. Obviously, these approaches rely on the availability of shared memory, and therefore do not scale beyond the size of a single computer.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Network Simulation: Leveraging the Strengths of Modern SMP-based Compute Clusters

Stoffers

Schmerling

Kunz

et al. 2014

Proceedings of the Seventh International Conference on Simulation Tools and Techniques

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Parallelization is crucial for efficient execution of large-scale network simulation. Today's computing clusters commonly used for that purpose are built from a large amount of multiprocessor machines. The traditional approach to utilize all CPU cores in such a system is to partition the network and distribute the partitions to the cores. This, however, does not incorporate the presence of shared memory into the design, such that messages between partitions on the same computing node have to be serialized and synchronization becomes more complex. In this paper, we present an approach that combines the shared-memory parallelization scheme Horizon [9] with the standard approach to distributed simulation to leverage the strengths of today's computing clusters. To further reduce the synchronization overhead, we introduce a novel synchronization algorithm that takes domain knowledge into account to reduce the number of synchronization points. In a case study with a UMTS LTE model, we show that both contributions combined enable much higher scalability achieving almost linear speedup when simulating 1,536 LTE cells on 1,536 CPU cores.

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“…These are blocking sender-side (BSS) CCT which is the original CCT algorithm, simple sender-side (SSS) CCT and receive-side (RS) CCT. A proof of correctness for BSS was presented in [11] and the proof correctness of SSS and RS follow from that.…”

Section: Cct Algorithmsmentioning

confidence: 99%

“…This paper presents two modified versions of the Critical Channel Traversing algorithm (CCT) [11]. The first is a simplification of the original that avoids busy waiting that could inhibit the performance of the original algorithm in some situations [7].…”

Section: Introductionmentioning

confidence: 99%