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E-raamat: 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-- 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-- 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.
Foreword xi
Preface xv
Contributors xix
1 State-of-the-Art Technologies for Large-Scale Computing
1(18)
Florian Feldhaus
Stefan Freitag
Chaker El Amrani
1.1 Introduction
1(1)
1.2 Grid Computing
2(4)
1.3 Virtualization
6(2)
1.4 Cloud Computing
8(4)
1.4.1 Drawbacks of Cloud Computing
9(1)
1.4.2 Cloud Interfaces
10(2)
1.5 Grid and Cloud: Two Complementary Technologies
12(1)
1.6 Modeling and Simulation of Grid and Cloud Computing
13(2)
1.6.1 GridSim and CloudSim Toolkits
14(1)
1.7 Summary and Outlook
15(1)
References
16(3)
2 The e-Infrastructure Ecosystem: Providing Local Support to Global Science
19(16)
Erwin Laure
Ake Edlund
2.1 The Worldwide e-Infrastructure Landscape
19(2)
2.2 BalticGrid: A Regional e-Infrastructure, Leveraging on the Global "Mothership" EGEE
21(4)
2.2.1 The BalticGrid Infrastructure
21(1)
2.2.2 BalticGrid Applications: Providing Local Support to Global Science
22(1)
2.2.3 The Pilot Applications
23(2)
2.2.4 BalticGrid's Support Model
25(1)
2.3 The EGEE Infrastructure
25(4)
2.3.1 The EGEE Production Service
26(2)
2.3.2 EGEE and BalticGrid: e-Infrastructures in Symbiosis
28(1)
2.4 Industry and e-Infrastructures: The Baltic Example
29(2)
2.4.1 Industry and Grids
29(1)
2.4.2 Industry and Clouds, Clouds and e-Infrastructures
30(1)
2.4.3 Clouds: A New Way to Attract SMEs and Start-Ups
30(1)
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)
2.5.1 Layers of the Ecosystem
32(1)
2.6 Summary
33(1)
Acknowledgments
34(1)
References
34(1)
3 Accelerated Many-Core GPU Computing for Physics and Astrophysics on Three Continents
35(24)
Rainer Spurzem
Peter Berczik
Ingo Berentzen
Wei Ge
Xiaowei Wang
Hsi-Yu Schive
Keigo Nitadori
Tsuyoshi Hamada
Jose Fiestas
3.1 Introduction
36(2)
3.2 Astrophysical Application for Star Clusters and Galactic Nuclei
38(2)
3.3 Hardware
40(1)
3.4 Software
41(1)
3.5 Results of Benchmarks
42(7)
3.6 Adaptive Mesh Refinement Hydrosimulations
49(1)
3.7 Physical Multiscale Discrete Simulation at IPE
49(4)
3.8 Discussion and Conclusions
53(1)
Acknowledgments
54(1)
References
54(5)
4 An Overview of the SimWorld Agent-Based Grid Experimentation System
59(22)
Matthias Scheutz
Jack J. Harris
4.1 Introduction
59(3)
4.2 System Architecture
62(5)
4.3 System Implementation
67(4)
4.3.1 Key Components
68(1)
4.3.2 Novel Features in SWAGES
69(2)
4.4 A SWAGES Case Study
71(3)
4.4.1 Research Questions and Simulation Model
71(1)
4.4.2 The Simulation Environment
72(1)
4.4.3 Simulation Runs in SWAGES
72(1)
4.4.4 Data Management and Visualization
73(1)
4.5 Discussion
74(4)
4.5.1 Automatic Parallelization of Agent-Based Models
75(1)
4.5.2 Integrated Data Management
76(1)
4.5.3 Automatic Error Detection and Recovery
76(1)
4.5.4 SWAGES Compared to Other Frameworks
76(2)
4.6 Conclusions
78(1)
References
78(3)
5 Repast HPC: A Platform for Large-Scale Agent-Based Modeling
81(30)
Nicholson Collier
Michael North
5.1 Introduction
81(1)
5.2 Agent Simulation
82(1)
5.3 Motivation and Related Work
82(8)
5.4 From Repast S to Repast HPC
90(2)
5.4.1 Agents as Objects
91(1)
5.4.2 Scheduling
91(1)
5.4.3 Modeling
91(1)
5.5 Parallelism
92(2)
5.6 Implementation
94(7)
5.6.1 Context
95(1)
5.6.2 RepastProcess
95(1)
5.6.3 Scheduler
96(1)
5.6.4 Distributed Network
97(1)
5.6.5 Distributed Grid
98(1)
5.6.6 Data Collection and Logging
99(1)
5.6.7 Random Number Generation and Properties
100(1)
5.7 Example Application: Rumor Spreading
101(6)
5.7.1 Performance Results
103(4)
5.8 Summary and Future Work
107(1)
References
107(4)
6 Building and Running Collaborative Distributed Multiscale Applications
111(20)
Katarzyna Rycerz
Marian Bubak
6.1 Introduction
111(1)
6.2 Requirements of Multiscale Simulations
112(4)
6.2.1 Interactions between Single-Scale Models
113(2)
6.2.2 Interoperability, Composability, and Reuse of Simulation Models
115(1)
6.3 Available Technologies
116(3)
6.3.1 Tools for Multiscale Simulation Development
116(1)
6.3.2 Support for Composability
117(1)
6.3.3 Support for Simulation Sharing
118(1)
6.4 An Environment Supporting the HLA Component Model
119(5)
6.4.1 Architecture of the CompoHLA Environment
119(1)
6.4.2 Interactions within the CompoHLA Environment
120(2)
6.4.3 HLA Components
122(2)
6.4.4 CompoHLA Component Users
124(1)
6.5 Case Study with the MUSE Application
124(3)
6.6 Summary and Future Work
127(1)
Acknowledgments
128(1)
References
129(2)
7 Large-Scale Data-Intensive Computing
131(10)
Mark Parsons
7.1 Digital Data: Challenge and Opportunity
131(1)
7.1.1 The Challenge
131(1)
7.1.2 The Opportunity
132(1)
7.2 Data-Intensive Computers
132(2)
7.3 Advanced Software Tools and Techniques
134(5)
7.3.1 Data Mining and Data Integration
134(1)
7.3.2 Making Data Mining Easier
135(2)
7.3.3 The ADMIRE Workbench
137(2)
7.4 Conclusion
139(1)
Acknowledgments
139(1)
References
139(2)
8 A Topology-Aware Evolutionary Algorithm for Reverse-Engineering Gene Regulatory Networks
141(22)
Martin Swain
Camille Coti
Johannes Mandel
Werner Dubitzky
8.1 Introduction
141(2)
8.2 Methodology
143(12)
8.2.1 Modeling GRNs
143(5)
8.2.2 QCG-OMPI
148(4)
8.2.3 A Topology-Aware Evolutionary Algorithm
152(3)
8.3 Results and Discussion
155(5)
8.3.1 Scaling and Speedup of the Topology-Aware Evolutionary Algorithm
155(3)
8.3.2 Reverse-Engineering Results
158(2)
8.4 Conclusions
160(1)
Acknowledgments
161(1)
References
161(2)
9 QosCosGrid e-Science Infrastructure for Large-Scale Complex System Simulations
163(24)
Krzysztof Kurowski
Bartosz Bosak
Piotr Grabowski
Mariusz Mamonski
Tomasz Piontek
George Kampis
Laszlo Gulyas
Camille Coti
Thomas Herault
Franck Cappello
9.1 Introduction
163(2)
9.2 Distributed and Parallel Simulations
165(3)
9.3 Programming and Execution Environments
168(6)
9.3.1 QCG-OMPI
169(2)
9.3.2 QCG-ProActive
171(3)
9.4 QCG Middleware
174(5)
9.4.1 QCG-Computing Service
175(1)
9.4.2 QCG-Notification and Data Movement Services
176(1)
9.4.3 QCG-Broker Service
177(2)
9.5 Additional QCG Tools
179(1)
9.5.1 Eclipse Parallel Tools Platform (PTP) for QCG
179(1)
9.6 QosCosGrid Science Gateways
180(2)
9.7 Discussion and Related Work
182(2)
References
184(3)
Glossary 187(8)
Index 195
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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