Large-Scale Simulation

Large-Scale Simulation: Models, Algorithms, and Applications

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Features

  • Summarizes the authors’ vast research on large-scale simulations
  • Presents advanced models, algorithms, and architectures for large-scale distributed simulation
  • Covers important applications of large-scale simulation for studying social phenomena
  • Describes related work, including simulation cloning, fault tolerance, and synchronization in federation communities

Summary

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

FUNDAMENTALS
Introduction
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

MIDDLEWARE AND SOFTWARE ARCHITECTURES
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

EVALUATION OF ALTERNATIVE SCENARIOS
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

APPLICATIONS
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

Index

Author Bio(s)

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.