Large-Scale Simulation

NOTE EDITORE

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.

SOMMARIO

FUNDAMENTALSIntroductionBackground Organization of the Book Background and FundamentalsHigh Level Architecture and Runtime InfrastructureCloning and ReplicationSimulation CloningSummary of Cloning and Replication Techniques Fault Tolerance Time Management Mechanisms for Federation Community MIDDLEWARE AND SOFTWARE ARCHITECTURESA Decoupled Federate ArchitectureProblem Statement Virtual Federate and Physical Federate Inside the Decoupled Architecture Federate Cloning ProcedureBenchmark Experiments and ResultsSummary Exploiting the Decoupled Federate Architecture Fault-Tolerant HLA-Based Distributed SimulationsIntroduction Decoupled Federate Architecture A Framework for Supporting Robust HLA-Based SimulationsExperiments and ResultsSummary Synchronization in Federation Community Networks Introduction HLA Federation CommunitiesTime Management in Federation Communities Synchronization Algorithms for Federation Community NetworksExperiments and ResultsSummary EVALUATION OF ALTERNATIVE SCENARIOSTheory and Issues in Distributed Simulation CloningDecision Points Active and Passive Cloning of Federates Entire versus Incremental Cloning Scenario Tree Summary Alternative Solutions for Cloning in HLA-Based Distributed SimulationSingle-Federation Solution versus Multiple-Federation Solution DDM versus Non-DDM in Single-Federation Solution Middleware Approach Benchmark Experiments and ResultsSummary Managing Scenarios Problem Statement Recursive Region Division Solution Point Region SolutionSummary Algorithms for Distributed Simulation CloningOverview of Simulation Cloning Infrastructure Passive Simulation Cloning Mapping Entities Incremental Distributed Simulation CloningSummary 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 SimulationHybrid Modeling and Simulation of Huge Crowd: A Case StudyExperiments and ResultsSummary Massively Parallel M&S of a Large Crowd with GPGPU Introduction Background and Notation The Hybrid Behavior Model A Case Study of Confrontation Operation SimulationConfrontation Operation Simulation Aided by GP-GPUSummary Index

AUTORE

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.

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9781138071971
• Dimensioni: 9.25 x 6.25 in Ø 0.80 lb
• Formato: Brossura
• Illustration Notes: 107 b/w images and 18 tables
• Pagine Arabe: 260