Large-Scale Computing -- Contents -- Foreword -- Preface -- Contributors -- Chapter 1: State-of-the-Art Technologies for Large-Scale Computing -- 1.1 INTRODUCTION -- 1.2 GRID COMPUTING -- 1.3 VIRTUALIZATION -- 1.4 CLOUD COMPUTING -- 1.4.1 Drawbacks of Cloud Computing -- 1.4.2 Cloud Interfaces -- 1.5 GRID AND CLOUD: TWO COMPLEMENTARY TECHNOLOGIES -- 1.6 MODELING AND SIMULATION OF GRID AND CLOUD COMPUTING -- 1.6.1 GridSim and CloudSim Toolkits -- 1.7 SUMMARY AND OUTLOOK -- REFERENCES -- Chapter 2: The e-Infrastructure Ecosystem: Providing Local Support to Global Science -- 2.1 THE WORLDWIDE E-INFRASTRUCTURE LANDSCAPE -- 2.2 BALTICGRID: A REGIONAL E-INFRASTRUCTURE, LEVERAGING ON THE GLOBAL "MOTHERSHIP" EGEE -- 2.2.1 The BalticGrid Infrastructure -- 2.2.2 BalticGrid Applications: Providing Local Support to Global Science -- 2.2.3 The Pilot Applications -- 2.2.4 BalticGrid's Support Model -- 2.3 THE EGEE INFRASTRUCTURE -- 2.3.1 The EGEE Production Service -- 2.3.2 EGEE and BalticGrid: e-Infrastructures in Symbiosis -- 2.4 INDUSTRY AND E-INFRASTRUCTURES: THE BALTIC EXAMPLE -- 2.4.1 Industry and Grids -- 2.4.2 Industry and Clouds, Clouds and e-Infrastructures -- 2.4.3 Clouds: A New Way to Attract SMEs and Start-Ups -- 2.5 THE FUTURE OF EUROPEAN E-INFRASTRUCTURES: THE EUROPEAN GRID INITIATIVE (EGI) AND THE PARTNERSHIP FOR ADVANCED COMPUTING IN EUROPE (PRACE) INFRASTRUCTURES -- 2.5.1 Layers of the Ecosystem -- 2.6 SUMMARY -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 3: Accelerated Many-Core GPU Computing for Physics and Astrophysics on Three Continents -- 3.1 INTRODUCTION -- 3.2 ASTROPHYSICAL APPLICATION FOR STAR CLUSTERS AND GALACTIC NUCLEI -- 3.3 HARDWARE -- 3.4 SOFTWARE -- 3.5 RESULTS OF BENCHMARKS -- 3.6 ADAPTIVE MESH REFINEMENT HYDROSIMULATIONS -- 3.7 PHYSICAL MULTISCALE DISCRETE SIMULATION AT IPE -- 3.8 DISCUSSION AND CONCLUSIONS -- ACKNOWLEDGMENTS.

REFERENCES -- Chapter 4: An Overview of the SimWorld Agent-Based Grid Experimentation System -- 4.1 INTRODUCTION -- 4.2 SYSTEM ARCHITECTURE -- 4.3 SYSTEM IMPLEMENTATION -- 4.3.1 Key Components -- 4.3.2 Novel Features in SWAGES -- 4.4 A SWAGES CASE STUDY -- 4.4.1 Research Questions and Simulation Model -- 4.4.2 The Simulation Environment -- 4.4.3 Simulation Runs in SWAGES -- 4.4.4 Data Management and Visualization -- 4.5 DISCUSSION -- 4.5.1 Automatic Parallelization of Agent-Based Models -- 4.5.2 Integrated Data Management -- 4.5.3 Automatic Error Detection and Recovery -- 4.5.4 SWAGES Compared to Other Frameworks -- 4.6 CONCLUSIONS -- REFERENCES -- Chapter 5: Repast HPC: A Platform for Large-Scale Agent-Based Modeling -- 5.1 INTRODUCTION -- 5.2 AGENT SIMULATION -- 5.3 MOTIVATION AND RELATED WORK -- 5.4 FROM REPAST S TO REPAST HPC -- 5.4.1 Agents as Objects -- 5.4.2 Scheduling -- 5.4.3 Modeling -- 5.5 PARALLELISM -- 5.6 IMPLEMENTATION -- 5.6.1 Context -- 5.6.2 RepastProcess -- 5.6.3 Scheduler -- 5.6.4 Distributed Network -- 5.6.5 Distributed Grid -- 5.6.6 Data Collection and Logging -- 5.6.7 Random Number Generation and Properties -- 5.7 EXAMPLE APPLICATION: RUMOR SPREADING -- 5.7.1 Performance Results -- 5.8 SUMMARY AND FUTURE WORK -- REFERENCES -- Chapter 6: Building and Running Collaborative Distributed Multiscale Applications -- 6.1 INTRODUCTION -- 6.2 REQUIREMENTS OF MULTISCALE SIMULATIONS -- 6.2.1 Interactions between Single-Scale Models -- 6.2.2 Interoperability, Composability, and Reuse of Simulation Models -- 6.3 AVAILABLE TECHNOLOGIES -- 6.3.1 Tools for Multiscale Simulation Development -- 6.3.2 Support for Composability -- 6.3.3 Support for Simulation Sharing -- 6.4 AN ENVIRONMENT SUPPORTING THE HLA COMPONENT MODEL -- 6.4.1 Architecture of the CompoHLA Environment -- 6.4.2 Interactions within the CompoHLA Environment -- 6.4.3 HLA Components.

6.4.4 CompoHLA Component Users -- 6.5 CASE STUDY WITH THE MUSE APPLICATION -- 6.6 SUMMARY AND FUTURE WORK -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 7: Large-Scale Data-Intensive Computing -- 7.1 DIGITAL DATA: CHALLENGE AND OPPORTUNITY -- 7.1.1 The Challenge -- 7.1.2 The Opportunity -- 7.2 DATA-INTENSIVE COMPUTERS -- 7.3 ADVANCED SOFTWARE TOOLS AND TECHNIQUES -- 7.3.1 Data Mining and Data Integration -- 7.3.2 Making Data Mining Easier -- 7.3.3 The ADMIRE Workbench -- 7.4 CONCLUSION -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 8: A Topology-Aware Evolutionary Algorithm for Reverse-Engineering Gene Regulatory Networks -- 8.1 INTRODUCTION -- 8.2 METHODOLOGY -- 8.2.1 Modeling GRNs -- 8.2.2 QCG-OMPI -- 8.2.3 A Topology-Aware Evolutionary Algorithm -- 8.3 RESULTS AND DISCUSSION -- 8.3.1 Scaling and Speedup of the Topology-Aware Evolutionary Algorithm -- 8.3.2 Reverse-Engineering Results -- 8.4 CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 9: QosCosGrid e-Science Infrastructure for Large-Scale Complex System Simulations -- 9.1 INTRODUCTION -- 9.2 DISTRIBUTED AND PARALLEL SIMULATIONS -- 9.3 PROGRAMMING AND EXECUTION ENVIRONMENTS -- 9.3.1 QCG-OMPI -- 9.3.2 QCG-ProActive -- 9.4 QCG MIDDLEWARE -- 9.4.1 QCG-Computing Service -- 9.4.2 QCG-Notification and Data Movement Services -- 9.4.3 QCG-Broker Service -- 9.5 ADDITIONAL QCG TOOLS -- 9.5.1 Eclipse Parallel Tools Platform (PTP) for QCG -- 9.6 QOSCOSGRID SCIENCE GATEWAYS -- 9.7 DISCUSSION AND RELATED WORK -- REFERENCES -- Glossary -- Index.