Informationen zum Autor 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. Klappentext This work 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. Along with presenting the fundamentals, the book covers middleware and software architecture for large-scale simulations as well as mechanisms that support quick evaluation of alternative scenarios. It also describes how distributed computing technologies and many-core architecture are used to study social phenomena. Zusammenfassung 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 mechanismssuch as simulation cloning methods and algorithmsthat 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. Inhaltsverzeichnis FUNDAMENTALS: Introduction. Background and Fundamentals. MIDDLEWARE AND SOFTWARE ARCHITECTURES: A Decoupled Federate Architecture. Fault-Tolerant HLA-Based Distributed Simulations. Synchronization in Federation Community Networks. EVALUATION OF ALTERNATIVE SCENARIOS: Theory and Issues in Distributed Simulation Cloning. Alternative Solutions for Cloning in HLA-Based Distributed Simulation. Managing Scenarios. Algorithms for Distributed Simulation Cloning. Experiments and Results of Simulation Cloning Algorithms. APPLICATIONS: Hybrid Modeling and Simulation of a Huge Crowd over an HGA. Massively Parallel M&S of a Large Crowd with GPGPU. Index. ...

Auteur

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.

Texte du rabat

This work 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. Along with presenting the fundamentals, the book covers middleware and software architecture for large-scale simulations as well as mechanisms that support quick evaluation of alternative scenarios. It also describes how distributed computing technologies and many-core architecture are used to study social phenomena.

Résumé

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 mechanismssuch as simulation cloning methods and algorithmsthat 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.

Contenu

FUNDAMENTALS: Introduction. Background and Fundamentals. MIDDLEWARE AND SOFTWARE ARCHITECTURES: A Decoupled Federate Architecture. Fault-Tolerant HLA-Based Distributed Simulations. Synchronization in Federation Community Networks. EVALUATION OF ALTERNATIVE SCENARIOS: Theory and Issues in Distributed Simulation Cloning. Alternative Solutions for Cloning in HLA-Based Distributed Simulation. Managing Scenarios. Algorithms for Distributed Simulation Cloning. Experiments and Results of Simulation Cloning Algorithms. APPLICATIONS: Hybrid Modeling and Simulation of a Huge Crowd over an HGA. Massively Parallel M&S of a Large Crowd with GPGPU. Index.