Large-Scale Simulation: Models, Algorithms, and Applications

Large-Scale Simulation: Models, Algorithms, and Applications

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Organized to facilitate intuitive learning, this step-by-step manual includes basic and advanced models and algorithms, and software architecture for the implementation of large-scale distributed simulation. An indispensible reference, it offers background information on performance evaluation, and it introduces new computing infrastructures such as Grids, SOA, and Clouds, with coverage of parallel and distributed computing technologies. The book also addresses middleware and software architecture, such as Decoupled Federate Architecture, and fault-tolerant mechanisms, grid-enabled simulation, and federation community. The book tackles simulation cloning methods and mechanisms that support quick evaluation of alternative scenarios, as well as important applications used in business processes, social phenomenology, and neuroscience research.

Product Details

ISBN-13: 9781439868867
Publisher: Taylor & Francis
Publication date: 06/15/2012
Edition description: New Edition
Pages: 259
Product dimensions: 6.00(w) x 9.20(h) x 0.80(d)

About the Author

Dan Chen is a professor and director of the Scientific Computing Lab at the China University of Geosciences. His research interests include computer-based modeling and simulation, high performance computing, and neuroinformatics.

Lizhe Wang is a professor at the Center for Earth Observation and Digital Earth, Chinese Academy of Sciences. Dr. Wang is also a "ChuTian Scholar" Chair Professor at the China University of Geosciences, a senior member of IEEE, and a member of ACM. His research interests include high performance computing, grid/cloud computing, and data-intensive computing.

Jingying Chen is a professor in the National Engineering Centre for e-Learning at Huazhong Normal University. Her research interests include intelligent systems, computer vision, and pattern recognition.

Table of Contents

Background Organization of the Book

Background and Fundamentals
High Level Architecture and Runtime Infrastructure Cloning and Replication Simulation Cloning Summary of Cloning and Replication Techniques Fault Tolerance Time Management Mechanisms for Federation Community

A Decoupled Federate Architecture
Problem Statement Virtual Federate and Physical Federate Inside the Decoupled Architecture Federate Cloning Procedure Benchmark Experiments and Results Summary Exploiting the Decoupled Federate Architecture

Fault-Tolerant HLA-Based Distributed Simulations
Introduction Decoupled Federate Architecture A Framework for Supporting Robust HLA-Based Simulations Experiments and Results Summary

Synchronization in Federation Community Networks
Introduction HLA Federation Communities Time Management in Federation Communities Synchronization Algorithms for Federation Community Networks Experiments and Results Summary

Theory and Issues in Distributed Simulation Cloning
Decision Points Active and Passive Cloning of Federates Entire versus Incremental Cloning Scenario Tree Summary

Alternative Solutions for Cloning in HLA-Based Distributed Simulation
Single-Federation Solution versus Multiple-Federation Solution DDM versus Non-DDM in Single-Federation Solution Middleware Approach Benchmark Experiments and Results Summary

Managing Scenarios
Problem Statement Recursive Region Division Solution Point Region Solution Summary

Algorithms for Distributed Simulation Cloning
Overview of Simulation Cloning Infrastructure Passive Simulation Cloning Mapping Entities Incremental Distributed Simulation Cloning Summary

Experiments and Results of Simulation Cloning Algorithms
An Application Example Configuration of Experiments Correctness of Distributed Simulation Cloning Efficiency of Distributed Simulation Cloning Scalability of Distributed Simulation Cloning Optimizing the Cloning Procedure Summary of Experiments and Results Achievements in Simulation Cloning

Hybrid Modeling and Simulation of a Huge Crowd over an HGA
Introduction Crowd Modeling and Simulation The Hierarchical Grid Architecture for Large Hybrid Simulation Hybrid Modeling and Simulation of Huge Crowd: A Case Study Experiments and Results Summary

Massively Parallel M&S of a Large Crowd with GPGPU
Introduction Background and Notation The Hybrid Behavior Model A Case Study of Confrontation Operation Simulation Confrontation Operation Simulation Aided by GP-GPU Summary


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