Modelling Environments for Distributed Simulation

Lees, Michael; Logan, Brian; Minson, Rob; Oguara, Ton; Théodoropoulos, Georgios

doi:10.1007/978-3-540-32259-7_8

Cited by 11 publications

(8 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Spheres of influence are dynamically determined on the basis of the mutual interactions among agents and information. In [27] shared data is maintained in a tuple-space. The tuple-space is partitioned by following a hierarchical schema based on the spheres of influence so as to avoid bottleneck in managing the shared data.…”

Section: Resultsmentioning

confidence: 99%

An approach for scalable parallel execution of ant algorithms

Cicirelli

Forestiero²,

Giordano³

et al. 2014

2014 International Conference on High Performance Computing &Amp; Simulation (HPCS)

View full text Add to dashboard Cite

This paper presents an approach for the efficient parallel/distributed execution of ant algorithms, based on multiagent systems. A very popular clustering problem, i.e., the spatially sorting of items belonging to a number of predefined classes, is taken as a use case. The approach consists in partitioning the problem space to a number of parallel nodes. Data consistency and conflict issues, which may arise when multiple agents concurrently access shared data, are transparently handled using a purposely developed notion of logical time. The developer remains in charge only of defining the behavior of the agents modeling the ants, without coping with issues related to parallel/distributed programming and performance optimization. Experimental results show that the approach is scalable and can be adopted to speed up the ant algorithm execution when the problem size is large, as may be in the case of massive data analysis and clustering.

show abstract

Section: Resultsmentioning

confidence: 99%

An approach for scalable parallel execution of ant algorithms

Cicirelli

Forestiero²,

Giordano³

et al. 2014

2014 International Conference on High Performance Computing &Amp; Simulation (HPCS)

View full text Add to dashboard Cite

show abstract

“…The efficient propagation of updates to the shared environment is a key problem for simulations of multiagent systems [Lees et al 2004]. Our approach currently makes no use of the data distribution management services provided by the RTI, and we plan to extend our current implementation of HLA AGENT to utilize DDM.…”

Section: Discussionmentioning

confidence: 99%

Distributed simulation of agent-based systems with HLA

Lees

Logan

Theodoropoulos

2007

ACM Trans. Model. Comput. Simul.

Self Cite

View full text Add to dashboard Cite

In this article we describe HLA AGENT, a tool for the distributed simulation of agent-based systems, which integrates the SIM AGENT agent toolkit and the High Level Architecture (HLA) simulator interoperability framework. HLA AGENT offers enhanced simulation scalability and allows interoperation with other HLA-compliant simulators, promoting simulation reuse. Using a simple Tileworld example, we show how HLA AGENT can be used to flexibly distribute a SIM AGENT simulation so as to exploit available computing resources. We present experimental results that illustrate the performance of HLA AGENT on a Linux cluster running a distributed version of Tileworld and compare this with the original nondistributed SIM AGENT version.

show abstract

“…To harness more computational resource, parallel computing can be used. There are many critical aspects of considerations on developing a parallel simulation, for example, time synchronization [7], partitioning and load balancing [28], and interest management [14]. The problem of time synchronization is addressed in this study.…”

Section: Introductionmentioning

confidence: 99%

An Asynchronous Synchronization Strategy for Parallel Large-scale Agent-based Traffic Simulations

Cai

Aydt³

et al. 2015

Proceedings of the 3rd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

Self Cite

View full text Add to dashboard Cite

Large-scale agent-based traffic simulation is a promising tool to study the road traffic and help solving traffic problems, such as congestion and high emission in megacities. Such simulation requires high computational resource which triggers the need for parallel computing. The parallelization of agent-based traffic simulations is generally performed by decomposing the simulation space into spatial subregions. The agent models contained by each subregion are executed by Logical Processes (LPs). As the simulated system evolves over the simulation time in individual LPs, synchronization among LPs is required due to data dependencies. Existing work has used global barriers for synchronization which is a type of synchronous synchronization method. However, global barriers have very low efficiency due to the waiting of processes at barriers. High synchronization overhead is still one of the major performance issues in parallel large-scale agent-based traffic simulations. In this paper, we proposed a novel asynchronous conservative synchronization strategy named Mutual Appointment (MA) to address this issue. MA removes global barriers and allows LPs to communicate individually. Since the efficiency of conservative synchronization relies on the lookahead of the simulated system, a heuristic was developed to increase the lookahead in agentbased traffic simulations. It takes advantage of the intrinsic uncertainties in traffic simulations. MA together with the lookahead heuristic forms the Relaxed Mutual Appointment (RMA) strategy. Its efficiency was investigated in the parallel agent-based traffic simulator SEMSim Traffic using real world traffic data. Experiment results showed that the MA Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. strategy improved the speed-up of the parallel simulation compared to the barrier method, and the RMA strategy further improved the MA strategy by reducing the number of synchronization messages significantly.

show abstract

Modelling Environments for Distributed Simulation

Cited by 11 publications

References 18 publications

An approach for scalable parallel execution of ant algorithms

An approach for scalable parallel execution of ant algorithms

Distributed simulation of agent-based systems with HLA

An Asynchronous Synchronization Strategy for Parallel Large-scale Agent-based Traffic Simulations

Contact Info

Product

Resources

About