Distributed and Parallel Systems: Cluster and Grid Computing
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Preface Foreword 1. Grids in Context 2. Computational Grids I Applications 3 Distributed Supercomputing Applications 4 Real-Time Widely Distributed Instrumentation Systems 5 Data-Intensive Computing 6 Teleimmersion II Programming Tools 7 Application-Specific Tools 8 Compilers, Languages, and Libraries 9 Object-Based Approaches 10 High-Performance Commodity Computing III Services 11 The Globus Toolkit 12 High-Performance Schedulers 13 High-Throughput Resource Management 14 Instrumentation and Measurement 15 Performance Analysis and Visualization 16 Security, Accounting, and Assurance IV Infrastructure 17 Computing Platforms 18 Network Protocols 19 Network Quality of Service 20 Operating Systems and Network Interfaces 21 Network Infrastructure 22 Testbed Bridges from Research to Infrastructure Glossary Bibliography Contributor Biographies
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Increasingly, parallel processing is being seen as the only cost-effective method for the fast solution of computationally large and data-intensive problems. The emergence of inexpensive parallel computers such as commodity desktop multiprocessors and clusters of workstations or PCs has made such parallel methods generally applicable, as have software standards for portable parallel programming. This sets the stage for substantial growth in parallel software.Data-intensive applications such as transaction processing and information retrieval, data mining and analysis and multimedia services have provided a new challenge for the modern generation of parallel platforms. Emerging areas such as computational biology and nanotechnology have implications for algorithms and systems development, while changes in architectures, programming models and applications have implications for how parallel platforms are made available to users in the form of grid-based services.This book takes into account these new developments as well as covering the more traditional problems addressed by parallel computers.Where possible it employs an architecture-independent view of the underlying platforms and designs algorithms for an abstract model. Message Passing Interface (MPI), POSIX threads and OpenMP have been selected as programming models and the evolving application mix of parallel computing is reflected in various examples throughout the book.
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This article presents an overview of a Cluster and Grid Computing course offered as part of the Master of Engineering in Distributed Computing program at the University of Melbourne. It describes the operation of the course and its evolution to fulfil the demand for professionals in an emerging field of distributed computing.
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I. Parallelism 1. Introduction 2. Parallel Computer Architectures 3. Parallel Programming Considerations II. Applications 4. General Application Issues 5. Parallel Computing in CFD 6. Parallel Computing in Environment and Energy 7. Parallel Computational Chemistry 8. Application Overviews III. Software technologies 9. Software Technologies 10. Message Passing and Threads 11. Parallel I/O 12. Languages and Compilers 13. Parallel Object-Oriented Libraries 14. Problem-Solving Environments 15. Tools for Performance Tuning and Debugging 16. The 2-D Poisson Problem IV. Enabling Technologies and Algorithms 17. Reusable Software and Algorithms 18. Graph Partitioning for Scientific Simulations 19. Mesh Generation 20. Templates and Numerical Linear Algebra 21. Software for the Scalable Solutions of PDEs 22. Parallel Continuous Optimization 23. Path Following in Scientific Computing 24. Automatic Differentiation V. Conclusion 25. Wrap-up and Features
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Structure, functionality, parameters and organization of the computing Grid in Poland is described, mainly from the perspective of high-energy particle physics community, currently its largest consumer and developer. It represents distributed Tier-2 in the worldwide Grid infrastructure. It also provides services and resources for data-intensive applications in other sciences.
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I CONCURRENT PROGRAMMING: 1. What is Concurrent Programming? 2. The Concurrent Programming Abstraction. 3. The Mutal Exclusion Problem. 4. Semaphores. 5. Monitors. 6. the Problem of Dining Philosophers. II DISTRIBUTED PROGRAMMING. 7. Distributed Programming Models. 8. Ada. 9. occam. 10. Linda. 11. Distributed Mutual Exclusion. 12. Distributed Termination. 13. The Byzantine Generals Problem. III. IMPLEMENTATION PRINCIPLES: 14. Single Processor Implementation. 15. Multi-processor Implementation. 16. Real-Time Programming. Appendix A: Ada Overview. B: Concurrent Programs in Ada. C: Implementation of the Ada Emulations. D: Distributed Algoriths in Ada. Biblography. Index.
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