Large-Scale Simulation : Models, Algorithms, and Applications book cover
1st Edition

Large-Scale Simulation
Models, Algorithms, and Applications

ISBN 9781138071971
Published April 21, 2017 by CRC Press
259 Pages 107 B/W Illustrations

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Book Description

Large-Scale Simulation: Models, Algorithms, and Applications gives you firsthand insight on the latest advances in large-scale simulation techniques. Most of the research results are drawn from the authors’ papers in top-tier, peer-reviewed, scientific conference proceedings and journals.

The first part of the book presents the fundamentals of large-scale simulation, including high-level architecture and runtime infrastructure. The second part covers middleware and software architecture for large-scale simulations, such as decoupled federate architecture, fault tolerant mechanisms, grid-enabled simulation, and federation communities. In the third part, the authors explore mechanisms—such as simulation cloning methods and algorithms—that support quick evaluation of alternative scenarios. The final part describes how distributed computing technologies and many-core architecture are used to study social phenomena.

Reflecting the latest research in the field, this book guides you in using and further researching advanced models and algorithms for large-scale distributed simulation. These simulation tools will help you gain insight into large-scale systems across many disciplines.

Table of Contents

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
Exploiting the Decoupled Federate Architecture

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

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

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

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

Managing Scenarios
Problem Statement
Recursive Region Division Solution
Point Region Solution

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

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
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

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


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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.