Large-Scale Computing Techniques for Complex System Simulations


Complex systems modeling and simulation approaches are being adopted in a growing number of sectors, including finance, economics, biology, astronomy, and many more. Technologies ranging from distributed computing to specialized hardware are explored and developed to address the computational requirements arising in complex systems simulations.

The aim of this book is to present a representative overview of contemporary large-scale computing technologies in the context of ...

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Large-Scale Computing Techniques for Complex System Simulations

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Complex systems modeling and simulation approaches are being adopted in a growing number of sectors, including finance, economics, biology, astronomy, and many more. Technologies ranging from distributed computing to specialized hardware are explored and developed to address the computational requirements arising in complex systems simulations.

The aim of this book is to present a representative overview of contemporary large-scale computing technologies in the context of complex systems simulations applications. The intention is to identify new research directions in this field and to provide a communications platform facilitating an exchange of concepts, ideas and needs between the scientists and technologist and complex system modelers. On the application side, the book focuses on modeling and simulation of natural and man-made complex systems. On the computing technology side, emphasis is placed on the distributed computing approaches, but supercomputing and other novel technologies are also considered.

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Meet the Author

Werner Dubitzky, PhD, is Chair of Bioinformatics at the Biomedical Sciences Research Institute in the Faculty of Life and Health Sciences at the University of Ulster. His research investigates systems biology, knowledge management in biology, grid computing, and data mining.

Krzysztof Kurowski, PhD, leads the Applications Department at Poznan Supercomputing and Networking Center in Poland. His research is focused on the modeling of advanced applications, scheduling, and resource management in networked environments.

Bernhard Schott, Dipl. Phys., is the EU-Research Program Manager for Platform Computing GmbH.

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Table of Contents

Foreword XI

Preface XV

Contributors XIX

1 State-of-the-Art Technologies for Large-Scale Computing Florian Feldhaus Stefan Freitag Choker ElAmrani 1

1.1 Introduction 1

1.2 Grid Computing 2

1.3 Virtualization 6

1.4 Cloud Computing 8

1.4.1 Drawbacks of Cloud Computing 9

1.4.2 Cloud Interfaces 10

1.5 Grid and Cloud: Two Complementary Technologies 12

1.6 Modeling and Simulation of Grid and Cloud Computing 13

1.6.1 GridSim and CloudSim Toolkits 14

1.7 Summary and Outlook 15 References 16

2 The e-Infrastructure Ecosystem: Providing Local Support to Global Science Erwin Laure Ake Edlund 19

2.1 The Worldwide e-Infrastructure Landscape 19

2.2 BalticGrid:A Regional e-Infrastructure, Leveraging on the Global "Mothership" EGEE 21

2.2.1 The BalticGrid Infrastructure 21

2.2.2 BalticGrid Applications: Providing Local Support to Global Science 22

2.2.3 The Pilot Applications 23

2.2.4 BalticGrid's Support Model 25

2.3 The EGEE Infrastructure 25

2.3.1 The EGEE Production Service 26

2.3.2 EGEE and BalticGrid: e-Infrastructures in Symbiosis 28

2.4 Industry and e-Infrastructures: The Baltic Example 29

2.4.1 Industry and Grids 29

2.4.2 Industry and Clouds, Clouds and e-Infrastructures 30

2.4.3 Clouds: A New Way to Attract SMEs and Start-Ups 30

2.5 The Future of European e-Infrastructures: The European Grid Initiative (EGI) and the Partnership for Advanced Computing in Europe (PRACE) Infrastructures 31

2.5.1 Layers of the Ecosystem 32

2.6 Summary 33

Acknowledgments 34

References 34

3 Accelerated Many-Core GPU Computing for Physics and Astrophysics on Three Continents Rainer Spurzem Peter Berczik, Ingo Berentzen Wei Ge Xiaowei Wang Hsi-Yu Schivet Keigo Nitadori Tsuyoshi Hamada Jose Fiestas 35

3.1 Introduction 36

3.2 Astrophysical Application for Star Clusters and Galactic Nuclei 38

3.3 Hardware 40

3.4 Software 41

3.5 Results of Benchmarks 42

3.6 Adaptive Mesh Refinement Hydrosimulations 49

3.7 Physical Multiscale Discrete Simulation at IPE 49

3.8 Discussion and Conclusions 53 Acknowledgments 54 References 54

4 An Overview of the SimWorld Agent-Based Grid Experimentation System Matthias Scheutz Jack J. Harris 59

4.1 Introduction 59

4.2 System Architecture 62

4.3 System Implementation 67

4.3.1 Key Components 68

4.3.2 Novel Features in SWAGES 69

4.4 A SWAGES Case Study 71

4.4.1 Research Questions and Simulation Model 71

4.4.2 The Simulation Environment 72

4.4.3 Simulation Runs in SWAGES 72

4.4.4 Data Management and Visualization 73

4.5 Discussion 74

4.5.1 Automatic Parallelization of Agent-Based Models 75

4.5.2 Integrated Data Management 76

4.5.3 Automatic Error Detection and Recovery 76

4.5.4 SWAGES Compared to Other Frameworks 76

4.6 Conclusions 78 References 78

5 Repast HPC: A Platform for Large-Scale Agent-Based Modeling Nicholson Collier Michael North 81

5.1 Introduction 81

5.2 Agent Simulation 82

5.3 Motivation and Related Work 82

5.4 From Repast S to Repast HPC 90

5.4.1 Agents as Objects 91

5.4.2 Scheduling 91

5.4.3 Modeling 91

5.5 Parallelism 92

5.6 Implementation 94

5.6.1 Context 95

5.6.2 RepastProcess 95

5.6.3 Scheduler 96

5.6.4 Distributed Network 97

5.6.5 Distributed Grid 98

5.6.6 Data Collection and Logging 99

5.6.7 Random Number Generation and Properties 100

5.7 Example Application: Rumor Spreading 101

5.7.1 Performance Results 103

5.8 Summary and Future Work 107

References 107

6. Building and Running Collaborative Distributed Multiscale Applications Katarzyna Rycerz Marian Bubak 111

6.1 Introduction 111

6.2 Requirements of Multiscale Simulations 112

6.2.1 Interactions between Single-Scale Models 113

6.2.2 Interoperability, Composability, and Reuse of Simulation Models 115

6.3 Available Technologies 116

6.3.1 Tools for Multiscale Simulation Development 116

6.3.2 Support for Composability 117

6.3.3 Support for Simulation Sharing 118

6.4 An Environment Supporting the HLA Component Model 119

6.4.1 Architecture of the CompoHLA Environment 119

6.4.2 Interactions within the CompoHLA Environment 120

6.4.3 HLA Components 122

6.4.4 CompoHLA Component Users 124

6.5 Case Study with the MUSE Application 124

6.6 Summary and Future Work 127

Acknowledgments 128

References 129

7 Large-Scale Data-Intensive Computing Mark Parsons 131

7.1 Digital Data: Challenge and Opportunity 131

7.1.1 The Challenge 131

7.1.2 The Opportunity 132

7.2 Data-Intensive Computers 132

7.3 Advanced Software Tools and Techniques 134

7.3.1 Data Mining and Data Integration 134

7.3.2 Making Data Mining Easier 135

7.3.3 The ADMIRE Workbench 137

7.4 Conclusion 139

Acknowledgments 139

References 139

8 A Topology-Aware Evolutionary Algorithm for Reverse-Engineering Gene Regulatory Networks Martin Swain Camitte Coti Johannes Mandel Werner Dubitzky 141

8.1 Introduction 141

8.2 Methodology 143

8.2.1 Modeling GRNs 143

8.2.2 QCG-OMPI 148

8.2.3 A Topology-Aware Evolutionary Algorithm 152

8.3 Results and Discussion 155

8.3.1 Scaling and Speedup of the Topology-Aware Evolutionary Algorithm 155

8.3.2 Reverse-Engineering Results 158

8.4 Conclusions 160

Acknowledgments 161

References 161

9 QosCosGrid e-Science Infrastructure for Large-Scale Complex System Simulations Krzysztof Kurowski Bartosz Bosak Piotr Grabowski Mariusz Mamonski Tomasz Piontek George Kampis Laszlo Gulyds Camille Coti Thomas Herault Franck Cappello 163

9.1 Introduction 163

9.2 Distributed and Parallel Simulations 165

9.3 Programming and Execution Environments 168

9.3.1 QCG-OMPI 169

9.3.2 QCG-ProActive 171

9.4 QCG Middleware 174

9.4.1 QCG-Computing Service 175

9.4.2 QCG-Notification and Data Movement Services 176

9.4.3 QCG-Broker Service 177

9.5 Additional QCG Tools 179

9.5.1 Eclipse Parallel Tools Platform (PTP) for QCG 179

9.6 QosCosGrid Science Gateways 180

9.7 Discussion and Related Work 182

References 184

Glossary 187

Index 195

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