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

Large-Scale Simulation

Models, Algorithms, and Applications, 1st Edition

By Dan Chen, Lizhe Wang, Jingying Chen

CRC Press

259 pages | 107 B/W Illus.

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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

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

About the Authors

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.

Subject Categories

BISAC Subject Codes/Headings:
COM051230
COMPUTERS / Software Development & Engineering / General
COM059000
COMPUTERS / Computer Engineering
TEC007000
TECHNOLOGY & ENGINEERING / Electrical