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E-raamat: Large-Scale Simulation: Models, Algorithms, and Applications

, (Huazhong Normal University, Wuhan, Peoples Republic of China), (China University of Geosciences, Wuhan, Peoples Republic of China)
  • Formaat: 259 pages
  • Ilmumisaeg: 19-Dec-2017
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781439868966
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Large-Scale Simulation: Models, Algorithms, and ApplicationsSuurem pilt
  • Formaat: 259 pages
  • Ilmumisaeg: 19-Dec-2017
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781439868966
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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.
List of Figures
xi
List of Tables
xv
Foreword xvii
Preface xix
About the Authors xxi
Acknowledgments xxiii
I Fundamentals
1(22)
1 Introduction
3(4)
1.1 Background
3(2)
1.2 Organization of the Book
5(2)
2 Background and Fundamentals
7(16)
2.1 High Level Architecture and Runtime Infrastructure
7(4)
2.2 Cloning and Replication
11(2)
2.2.1 Cloning in Programming Languages
11(1)
2.2.2 Data Replication in Distributed Systems
11(1)
2.2.3 Agent Cloning in Multi-Agent Systems
12(1)
2.2.4 Object Replication in Parallel Object-Oriented Environments
13(1)
2.2.5 Fault Tolerance Using Replication
13(1)
2.3 Simulation Cloning
13(4)
2.3.1 Cloning in Rare Event Simulations
14(1)
2.3.2 Multitrajectory Simulations
14(1)
2.3.3 Cloning in Simulation Software Packages
15(1)
2.3.4 Parallel Simulation Cloning
15(1)
2.3.5 Cloning of HLA-Compliant Federates
16(1)
2.3.6 Fault-Tolerant Distributed Simulation
17(1)
2.4 Summary of Cloning and Replication Techniques
17(1)
2.5 Fault Tolerance
18(2)
2.6 Time Management Mechanisms for Federation Community
20(3)
II Middleware and Software Architectures
23(80)
3 A Decoupled Federate Architecture
25(20)
3.1 Problem Statement
25(2)
3.2 Virtual Federate and Physical Federate
27(2)
3.3 Inside the Decoupled Architecture
29(4)
3.4 Federate Cloning Procedure
33(2)
3.5 Benchmark Experiments and Results
35(4)
3.5.1 Experiment Design
35(2)
3.5.2 Latency Benchmark Results
37(1)
3.5.3 Time Advancement Benchmark Results
37(2)
3.6 Exploiting the Decoupled Federate Architecture
39(4)
3.6.1 Web/Grid-Enabled Architecture
40(2)
3.6.2 Load Balancing
42(1)
3.7 Summary
43(2)
4 Fault-Tolerant HLA-Based Distributed Simulations
45(24)
4.1 Introduction
46(2)
4.2 Decoupled Federate Architecture
48(1)
4.3 A Framework for Supporting Robust HLA-Based Simulations
49(9)
4.3.1 Fault-Tolerant Model
50(2)
4.3.2 Dealing with In-Transit Events
52(2)
4.3.3 Fossil Collection
54(2)
4.3.4 Optimizing the Failure Recovery Procedure
56(2)
4.4 Experiments and Results
58(9)
4.4.1 Configuration of Experiments
59(1)
4.4.2 Correctness of Fault-Tolerant Model
60(2)
4.4.3 Efficiency of Fault-Tolerant Model
62(2)
4.4.4 Scalability of the Fault-Tolerant Model
64(1)
4.4.5 User Transparency and Related Issues
65(2)
4.5 Summary
67(2)
5 Synchronization in Federation Community Networks
69(34)
5.1 Introduction
70(3)
5.2 HLA Federation Communities
73(5)
5.2.1 Construction Approaches
73(1)
5.2.2 Architectures of Federation Community Networks
74(2)
5.2.2.1 Proposed Internal Architecture of the Gateway Federates
76(1)
5.2.3 Grid-Enabled Federation Community
76(2)
5.3 Time Management in Federation Communities
78(3)
5.3.1 Problem Statement
80(1)
5.4 Synchronization Algorithms for Federation Community Networks
81(11)
5.4.1 Synchronization Algorithms
83(5)
5.4.2 Proof
88(1)
5.4.2.1 Compliance to HLA Rules
88(1)
5.4.2.2 Deadlock Free
89(2)
5.4.2.3 Correct TSO Event Transmissions
91(1)
5.5 Experiments and Results
92(8)
5.5.1 Experiments on Multiple-Layer Federation Community Networks
92(3)
5.5.2 Experiments on Peer-to-Peer Federation Community Networks
95(1)
5.5.3 Experiments on Grid-Enabled Federation Community Networks
96(4)
5.6 Summary
100(3)
III Evaluation of Alternative Scenarios
103(76)
6 Theory and Issues in Distributed Simulation Cloning
105(10)
6.1 Decision Points
105(1)
6.2 Active and Passive Cloning of Federates
106(1)
6.3 Entire versus Incremental Cloning
106(4)
6.3.1 Shared Clones
107(1)
6.3.2 Theory and Issues in Incremental Cloning
108(2)
6.4 Scenario Tree
110(2)
6.5 Summary
112(3)
7 Alternative Solutions for Cloning in HLA-Based Distributed Simulation
115(16)
7.1 Single-Federation Solution versus Multiple-Federations Solution
115(2)
7.2 DDM versus Non-DDM in Single-Federation Solution
117(2)
7.3 Middleware Approach
119(1)
7.4 Benchmark Experiments and Results
120(7)
7.4.1 Experiment Design
121(2)
7.4.2 Benchmark Results and Analysis
123(1)
7.4.3 Comparing Alternative Cloning Solutions Using TSO Federates
124(2)
7.4.4 Comparing Alternative Cloning Solutions Using RO Federates
126(1)
7.4.5 Comparing Alternative Cloning Solutions Using Time Advancement Benchmark Federates
127(1)
7.5 Summary
127(4)
8 Managing Scenarios
131(10)
8.1 Problem Statement
131(3)
8.2 Recursive Region Division Solution
134(3)
8.3 Point Region Solution
137(2)
8.4 Summary
139(2)
9 Algorithms for Distributed Simulation Cloning
141(22)
9.1 Overview of Simulation Cloning Infrastructure
141(3)
9.2 Active Simulation Cloning
144(6)
9.3 Passive Simulation Cloning
150(1)
9.4 Mapping Entities
151(3)
9.5 Incremental Distributed Simulation Cloning
154(6)
9.5.1 Illustrating Incremental Distributed Simulation Cloning
154(2)
9.5.2 Managing Shared Clones
156(4)
9.6 Summary
160(3)
10 Experiments and Results of Simulation Cloning Algorithms
163(16)
10.1 Application Example
163(1)
10.2 Configuration of Experiments
164(1)
10.3 Correctness of Distributed Simulation Cloning
165(2)
10.4 Efficiency of Distributed Simulation Cloning
167(3)
10.5 Scalability of Distributed Simulation Cloning
170(1)
10.6 Optimizing the Cloning Procedure
171(3)
10.7 Summary of Experiments and Results
174(1)
10.8 Achievements in Simulation Cloning
175(4)
IV Applications
179(40)
11 Hybrid Modeling and Simulation of a Huge Crowd over an HGA
181(20)
11.1 Introduction
181(2)
11.2 Crowd Modeling and Simulation
183(1)
11.3 Hierarchical Grid Architecture for Large Hybrid Simulation
184(3)
11.3.1 Grid System Architecture
184(1)
11.3.2 HLA-Based Simulation Model
184(2)
11.3.3 Hierarchical Grid Simulation Architecture: Overview
186(1)
11.4 Hybrid Modeling and Simulation of Huge Crowd: A Case Study
187(8)
11.4.1 Huge Crowd Scenario
187(2)
11.4.2 Simulation Models
189(1)
11.4.2.1 Pedestrian Agent Model
189(1)
11.4.2.2 Computational Model of the Crowd Aggregated in the Assembly Area
190(1)
11.4.2.3 Vehicle Agent Model
191(1)
11.4.3 Crowd Simulation over the Hybrid Grid Simulation Infrastructure
192(3)
11.5 Experiments and Results
195(4)
11.5.1 Communication Latency
196(1)
11.5.2 Crowd Simulation Outputs
196(2)
11.5.3 Performance Evaluation
198(1)
11.6 Summary
199(2)
12 Massively Parallel Modeling & Simulation of a Large Crowd with GPGPU
201(18)
12.1 Introduction
201(2)
12.2 Background and Notation
203(2)
12.3 Hybrid
205(3)
12.4 Case Study of Confrontation Operation Simulation
208(3)
12.4.1 Simulation of a Crowd in a Confrontation Operation
208(1)
12.4.2 Dynamics Analysis via Entropy Calculation
209(2)
12.5 Aided by GPGPU
211(5)
12.5.1 Parallelization of Crowd Simulation
211(1)
12.5.2 Evaluation of Performance and Energy Efficiency
212(1)
12.5.2.1 GPGPU-Aided Confrontation Operation Simulation
212(2)
12.5.2.2 Performance Evaluation and Energy Efficiency Analysis
214(2)
12.6 Summary
216(3)
References 219(12)
Index 231
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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