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…mehr
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
Foreword xi Preface xv Contributors xix 1. State-of-the-Art Technologies for Large-Scale Computing 1 Florian Feldhaus, Stefan Freitag, and Chaker El Amrani 1.1 Introduction 1 1.2 Grid Computing 2 1.3 Virtualization 6 1.4 Cloud Computing 8 1.5 Grid and Cloud: Two Complementary Technologies 12 1.6 Modeling and Simulation of Grid and Cloud Computing 13 1.7 Summary and Outlook 15 References 16 2. The e-Infrastructure Ecosystems: Providing Local Support to Global Science 19 Erwin Laure and Åke Edlund 2.1 The Worldwide e-Infrastructure Landscape 19 2.2 BalticGrid: A Regional e-Infrastructure, Leveraging on the Global "Mothership" EGEE 21 2.3 The EGEE Infrastructure 25 2.4 Industry and e-Infrastructures: The Baltic Example 29 2.5 The Future of European e-Infrastructures: The European Grid Initiative (EGI) and the Partnership for Advanced Computing in Europe (PRACE) Infrastructures 31 2.6 Summary 33 Acknowledgments 34 References 34 3. Accelerated Many-Core GPU Computing for Physics and Astrophysics on Three Continents 35 Rainer Spurzem, Peter Berczik, Ingo Berentzen, Wei Ge, Xiaowei Wang, Hsi-Yu Schive, Keigo Nitadori, Tsuyoshi Hamada, and José Fiestas 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 Systems 59 Matthew Scheutz and Jack J. Harris 4.1 Introduction 59 4.2 System Architecture 62 4.3 System Implementation 67 4.4 A SWAGES Case Study 71 4.5 Discussion 74 4.6 Conclusions 78 References 78 5. Repast HPC: A Platform for Large-Scale Agent-Based Modeling 81 Nicholson Collier and Michael North 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 5.5 Parallelism 92 5.6 Implementation 94 5.7 Example Application: Rumor Spreading 101 5.8 Summary and Future Work 107 References 107 6. Building and Running Collaborative Distributed Multiscale Applications 111 Katarzyna Rycerz and Marian Bubak 6.1 Introduction 111 6.2 Requirements of Multiscale Simulations 112 6.3 Available Technologies 116 6.4 An Environment Supporting the HLA Component Model 119 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 131 Mark Parsons 7.1 Digital Data: Challenge and Opportunity 131 7.2 Data-Intensive Computers 132 7.3 Advanced Software Tools and Techniques 134 7.4 Conclusion 139 Acknowledgments 139 References 139 8. A Topolpgy-Aware Evolutionary Algorithm for Reverse-Engineering Gene Regulatory Networks 141 Martin Swain, Camille Coti, Johannes Mandel, and Werner Dubitzky 8.1 Introduction 141 8.2 Methodology 143 8.3 Results and Discussion 155 8.4 Conclusions 160 Acknowledgments 161 References 161 9. QosCosGrid e-Science Infrastructure for Large-Scale Complex System Simulations 163 Krzysztof Kurowski, Bartosz Bosak, Piotr Grabowski, Mariusz Mamonski, Tomasz Piontek, George Kampis, László Gulyás, Camille Coti, Thomas Herault, and Franck Cappello 9.1 Introduction 163 9.2 Distributed and Parallel Simulations 165 9.3 Programming and Execution Environments 168 9.4 QCG Middleware 174 9.5 Additional QCG Tools 179 9.6 QosCosGrid Science Gateways 180 9.7 Discussion and Related Work 182 References 184 Glossary 187 Index 195
Foreword xi Preface xv Contributors xix 1. State-of-the-Art Technologies for Large-Scale Computing 1 Florian Feldhaus, Stefan Freitag, and Chaker El Amrani 1.1 Introduction 1 1.2 Grid Computing 2 1.3 Virtualization 6 1.4 Cloud Computing 8 1.5 Grid and Cloud: Two Complementary Technologies 12 1.6 Modeling and Simulation of Grid and Cloud Computing 13 1.7 Summary and Outlook 15 References 16 2. The e-Infrastructure Ecosystems: Providing Local Support to Global Science 19 Erwin Laure and Åke Edlund 2.1 The Worldwide e-Infrastructure Landscape 19 2.2 BalticGrid: A Regional e-Infrastructure, Leveraging on the Global "Mothership" EGEE 21 2.3 The EGEE Infrastructure 25 2.4 Industry and e-Infrastructures: The Baltic Example 29 2.5 The Future of European e-Infrastructures: The European Grid Initiative (EGI) and the Partnership for Advanced Computing in Europe (PRACE) Infrastructures 31 2.6 Summary 33 Acknowledgments 34 References 34 3. Accelerated Many-Core GPU Computing for Physics and Astrophysics on Three Continents 35 Rainer Spurzem, Peter Berczik, Ingo Berentzen, Wei Ge, Xiaowei Wang, Hsi-Yu Schive, Keigo Nitadori, Tsuyoshi Hamada, and José Fiestas 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 Systems 59 Matthew Scheutz and Jack J. Harris 4.1 Introduction 59 4.2 System Architecture 62 4.3 System Implementation 67 4.4 A SWAGES Case Study 71 4.5 Discussion 74 4.6 Conclusions 78 References 78 5. Repast HPC: A Platform for Large-Scale Agent-Based Modeling 81 Nicholson Collier and Michael North 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 5.5 Parallelism 92 5.6 Implementation 94 5.7 Example Application: Rumor Spreading 101 5.8 Summary and Future Work 107 References 107 6. Building and Running Collaborative Distributed Multiscale Applications 111 Katarzyna Rycerz and Marian Bubak 6.1 Introduction 111 6.2 Requirements of Multiscale Simulations 112 6.3 Available Technologies 116 6.4 An Environment Supporting the HLA Component Model 119 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 131 Mark Parsons 7.1 Digital Data: Challenge and Opportunity 131 7.2 Data-Intensive Computers 132 7.3 Advanced Software Tools and Techniques 134 7.4 Conclusion 139 Acknowledgments 139 References 139 8. A Topolpgy-Aware Evolutionary Algorithm for Reverse-Engineering Gene Regulatory Networks 141 Martin Swain, Camille Coti, Johannes Mandel, and Werner Dubitzky 8.1 Introduction 141 8.2 Methodology 143 8.3 Results and Discussion 155 8.4 Conclusions 160 Acknowledgments 161 References 161 9. QosCosGrid e-Science Infrastructure for Large-Scale Complex System Simulations 163 Krzysztof Kurowski, Bartosz Bosak, Piotr Grabowski, Mariusz Mamonski, Tomasz Piontek, George Kampis, László Gulyás, Camille Coti, Thomas Herault, and Franck Cappello 9.1 Introduction 163 9.2 Distributed and Parallel Simulations 165 9.3 Programming and Execution Environments 168 9.4 QCG Middleware 174 9.5 Additional QCG Tools 179 9.6 QosCosGrid Science Gateways 180 9.7 Discussion and Related Work 182 References 184 Glossary 187 Index 195
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