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.
Organization of the Book
Background and Fundamentals
High Level Architecture and Runtime Infrastructure
Cloning and Replication
Summary of Cloning and Replication Techniques
Time Management Mechanisms for Federation Community
MIDDLEWARE AND SOFTWARE ARCHITECTURES
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
EVALUATION OF ALTERNATIVE SCENARIOS
Alternative Solutions for Cloning in HLA-Based Distributed Simulation
Single-Federation Solution versus Multiple-Federation Solution
DDM versus Non-DDM in Single-Federation Solution
Benchmark Experiments and Results
Recursive Region Division Solution
Point Region Solution
Algorithms for Distributed Simulation Cloning
Overview of Simulation Cloning Infrastructure
Passive Simulation Cloning
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
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
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.
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