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Parallel Processing for Scientific Computing

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Abstract

List of Figures List of Tables Preface 1. Frontiers of Scientific Computing. An Overview Part I. Performance Modeling, Analysis and Optimization 2. Performance Analysis. From Art to Science 3. Approaches to Architecture-Aware Parallel Scientific Computation 4. Achieving High Performance on the BlueGene/L Supercomputer 5. Performance Evaluation and Modeling of Ultra-Scale Systems Part II. Parallel Algorithms and Enabling Technologies 6. Partitioning and Load Balancing 7. Combinatorial Parallel and Scientific Computing 8. Parallel Adaptive Mesh Refinement 9. Parallel Sparse Solvers, Preconditioners, and Their Applications 10. A Survey of Parallelization Techniques for Multigrid Solvers 11. Fault Tolerance in Large-Scale Scientific Computing Part III. Tools and Frameworks for Parallel Applications 12. Parallel Tools and Environments. A Survey 13. Parallel Linear Algebra Software 14. High-Performance Component Software Systems 15. Integrating Component-Based Scientific Computing Software Part IV. Applications of Parallel Computing 16. Parallel Algorithms for PDE-Constrained Optimization 17. Massively Parallel Mixed-Integer Programming 18. Parallel Methods and Software for Multicomponent Simulations 19. Parallel Computational Biology 20. Opportunities and Challenges for Parallel Computing in Science and Engineering Index.

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List of Figures List of Tables Preface 1. Frontiers of Scientific Computing. An Overview Part I. Performance Modeling, Analysis and Optimization 2. Performance Analysis. From Art to Science 3. Approaches to Architecture-Aware Parallel Scientific Computation 4. Achieving High Performance on the BlueGene/L Supercomputer 5. Performance Evaluation and Modeling of Ultra-Scale Systems Part II. Parallel Algorithms and Enabling Technologies 6. Partitioning and Load Balancing 7. Combinatorial Parallel and Scientific Computing 8. Parallel Adaptive Mesh Refinement 9. Parallel Sparse Solvers, Preconditioners, and Their Applications 10. A Survey of Parallelization Techniques for Multigrid Solvers 11. Fault Tolerance in Large-Scale Scientific Computing Part III. Tools and Frameworks for Parallel Applications 12. Parallel Tools and Environments. A Survey 13. Parallel Linear Algebra Software 14. High-Performance Component Software Systems 15. Integrating Component-Based Scientific Computing Software Part IV. Applications of Parallel Computing 16. Parallel Algorithms for PDE-Constrained Optimization 17. Massively Parallel Mixed-Integer Programming 18. Parallel Methods and Software for Multicomponent Simulations 19. Parallel Computational Biology 20. Opportunities and Challenges for Parallel Computing in Science and Engineering Index.

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Available abstract

List of Figures List of Tables Preface 1. Frontiers of Scientific Computing. An Overview Part I. Performance Modeling, Analysis and Optimization 2. Performance Analysis. From Art to Science 3. Approaches to Architecture-Aware Parallel Scientific Computation 4. Achieving High Performance on the BlueGene/L Supercomputer 5. Performance Evaluation and Modeling of Ultra-Scale Systems Part II. Parallel Algorithms and Enabling Technologies 6. Partitioning and Load Balancing 7. Combinatorial Parallel and Scientific Computing 8. Parallel Adaptive Mesh Refinement 9. Parallel Sparse Solvers, Preconditioners, and Their Applications 10. A Survey of Parallelization Techniques for Multigrid Solvers 11. Fault Tolerance in Large-Scale Scientific Computing Part III. Tools and Frameworks for Parallel Applications 12. Parallel Tools and Environments. A Survey 13. Parallel Linear Algebra Software 14. High-Performance Component Software Systems 15. Integrating Component-Based Scientific Computing Software Part IV. Applications of Parallel Computing 16. Parallel Algorithms for PDE-Constrained Optimization 17. Massively Parallel Mixed-Integer Programming 18. Parallel Methods and Software for Multicomponent Simulations 19. Parallel Computational Biology 20. Opportunities and Challenges for Parallel Computing in Science and Engineering Index.

Key concepts: Computer science, Parallel computing, Supercomputer, Massively parallel, Data-intensive computing, Parallel algorithm, Computational science, Parallel processing

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