HIGH-PERFORMANCE HETEROGENEOUS COMPUTING -- CONTENTS -- PREFACE -- ACKNOWLEDGMENTS -- PART I HETEROGENEOUS PLATFORMS: TAXONOMY, TYPICAL USES, AND PROGRAMMING ISSUES -- 1. Heterogeneous Platforms and Their Uses -- 1.1 Taxonomy of Heterogeneous Platforms -- 1.2 Vendor-Designed Heterogeneous Systems -- 1.3 Heterogeneous Clusters -- 1.4 Local Network of Computers (LNC) -- 1.5 Global Network of Computers (GNC) -- 1.6 Grid-Based Systems -- 1.7 Other Heterogeneous Platforms -- 1.8 Typical Uses of Heterogeneous Platforms -- 1.8.1 Traditional Use -- 1.8.2 Parallel Computing -- 1.8.3 Distributed Computing -- 2. Programming Issues -- 2.1 Performance -- 2.2 Fault Tolerance -- 2.3 Arithmetic Heterogeneity -- PART II PERFORMANCE MODELS OF HETEROGENEOUS PLATFORMS AND DESIGN OF HETEROGENEOUS ALGORITHMS -- 3. Distribution of Computations with Constant Performance Models of Heterogeneous Processors -- 3.1 Simplest Constant Performance Model of Heterogeneous Processors and Optimal Distribution of Independent Units of Computation with This Model -- 3.2 Data Distribution Problems with Constant Performance Models of Heterogeneous Processors -- 3.3 Partitioning Well-Ordered Sets with Constant Performance Models of Heterogeneous Processors -- 3.4 Partitioning Matrices with Constant Performance Models of Heterogeneous Processors -- 4. Distribution of Computations with Nonconstant Performance Models of Heterogeneous Processors -- 4.1 Functional Performance Model of Heterogeneous Processors -- 4.2 Data Partitioning with the Functional Performance Model of Heterogeneous Processors -- 4.3 Other Nonconstant Performance Models of Heterogeneous Processors -- 4.3.1 Stepwise Functional Model -- 4.3.2 Functional Model with Limits on Task Size -- 4.3.3 Band Model -- 5. Communication Performance Models for High-Performance Heterogeneous Platforms.

5.1 Modeling the Communication Performance for Scientific Computing: The Scope of Interest -- 5.2 Communication Models for Parallel Computing on Heterogeneous Clusters -- 5.3 Communication Performance Models for Local and Global Networks of Computers -- 6. Performance Analysis of Heterogeneous Algorithms -- 6.1 Efficiency Analysis of Heterogeneous Algorithms -- 6.2 Scalability Analysis of Heterogeneous Algorithms -- PART III PERFORMANCE: IMPLEMENTATION AND SOFTWARE -- 7. Implementation Issues -- 7.1 Portable Implementation of Heterogeneous Algorithms and Self-Adaptable Applications -- 7.2 Performance Models of Heterogeneous Platforms: Estimation of Parameters -- 7.2.1 Estimation of Constant Performance Models of Heterogeneous Processors -- 7.2.2 Estimation of Functional and Band Performance Models of Heterogeneous Processors -- 7.2.3 Benchmarking of Communication Operations -- 7.3 Performance Models of Heterogeneous Algorithms and Their Use in Applications and Programming Systems -- 7.4 Implementation of Homogeneous Algorithms for Heterogeneous Platforms -- 8. Programming Systems for High-Performance Heterogeneous Computing -- 8.1 Parallel Programming Systems for Heterogeneous Platforms -- 8.2 Traditional Parallel Programming Systems -- 8.2.1 Message-Passing Programming Systems -- 8.2.2 Linda -- 8.2.3 HPF -- 8.3 Heterogeneous Parallel Programming Systems -- 8.4 Distributed Programming Systems -- 8.4.1 NetSolve -- 8.4.2 Nimrod -- 8.4.3 Java -- 8.4.4 GridRPC -- PART IV APPLICATIONS -- 9. Numerical Linear Algebra Software for Heterogeneous Clusters -- 9.1 HeteroPBLAS: Introduction and User Interface -- 9.2 HeteroPBLAS: Software Design -- 9.3 Experiments with HeteroPBLAS -- 10. Parallel Processing of Remotely Sensed Hyperspectral Images on Heterogeneous Clusters -- 10.1 Hyperspectral Imaging: Introduction and Parallel Techniques.

10.2 A Parallel Algorithm for Analysis of Hyperspectral Images and Its Implementation for Heterogeneous Clusters -- 10.3 Experiments with the Heterogeneous Hyperspectral Imaging Application -- 10.4 Conclusion -- 11. Simulation of the Evolution of Clusters of Galaxies on Heterogeneous Computational Grids -- 11.1 Hydropad: A Simulator of Galaxies' Evolution -- 11.2 Enabling Hydropad for Grid Computing -- 11.2.1 GridRPC Implementation of the Hydropad -- 11.2.2 Experiments with the GridSolve-Enabled Hydropad -- 11.3 SmartGridSolve and Hydropad -- 11.3.1 SmartGridSolve Implementation of the Hydropad -- 11.3.2 Experiments with the SmartGridSolve-Enabled Hydropad -- 11.4 Acknowledgment -- PART V FUTURE TRENDS -- 12. Future Trends in Computing -- 12.1 Introduction -- 12.2 Computational Resources -- 12.2.1 Complex and Heterogeneous Parallel Systems -- 12.2.2 Intel-ization of the Processor Landscape -- 12.2.3 New Architectures on the Horizon -- 12.3 Applications -- 12.4 Software -- 12.5 Some Important Concepts for the Future -- 12.5.1 Heterogeneous Hardware Environments -- 12.5.2 Software Architecture -- 12.5.3 Open Source -- 12.5.4 New Applications -- 12.5.5 Verification and Validation -- 12.5.6 Data -- 12.6 2009 and Beyond -- REFERENCES -- APPENDICES -- Appendix A Appendix to Chapter 3 -- A.1 Proof of Proposition 3.1 -- A.2 Proof of Proposition 3.5 -- Appendix B Appendix to Chapter 4 -- B.1 Proof of Proposition 4.1 -- B.2 Proof of Proposition 4.2 -- B.3 Proof of Proposition 4.3 -- B.4 Functional Optimization Problem with Optimal Solution, Locally Nonoptimal -- INDEX.