Jack Dongarra, Alexey L. Lastovetsky

High Performance Heterogeneous Computing (eBook, PDF)

• Format: PDF

Produktbeschreibung

An analytical overview of the state of the art, open problems, and future trends in heterogeneous parallel and distributed computing This book provides an overview of the ongoing academic research, development, and uses of heterogeneous parallel and distributed computing in the context of scientific computing. Presenting the state of the art in this challenging and rapidly evolving area, the book is organized in five distinct parts: * Heterogeneous Platforms: Taxonomy, Typical Uses, and Programming Issues * Performance Models of Heterogeneous Platforms and Design of Heterogeneous Algorithms * Performance: Implementation and Software * Applications * Future Tre High Performance Heterogeneous Computing is a valuable¿reference for researchers and practitioners in the area of high performance heterogeneous computing. It also serves as an excellent supplemental text for graduate and postgraduate courses in related areas.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 280
• Erscheinungstermin: 17. August 2009
• Englisch
• ISBN-13: 9780470508190
• Artikelnr.: 37293263

Autorenporträt

Alexey L. Lastovetsky, PhD, is a Senior Lecturer at the School of Computer Science and Informatics at the University College Dublin (UCD), where he is also the founder and Director of the Heterogeneous Computing Laboratory. His main research interests include algorithms, models, and programming tools for high performance heterogeneous computing. He is the author of mpC, the first parallel programming language for heterogeneous networks of computers. He has published over ninety technical papers in refereed journals and edited books and proceedings for international conferences. He is also the author of Parallel Computing on Heterogeneous Networks (Wiley). Jack J. Dongarra, PhD, is University Distinguished Professor of Computer Science in the Electrical Engineering and Computer Science Department at the University of Tennessee and serves as Distinguished Research Staff in the Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL). He is a Turing Fellow in the Computer Science and Mathematics Schools at the University of Manchester and an Adjunct Professor in the Computer Science Department at Rice University. He specializes in numerical algorithms in linear algebra, parallel computing, the use of advanced computer architectures, programming methodology, and tools for parallel computers. His research includes the development, testing, and documentation of high quality mathematical software. He has published approximately 200 articles, papers, reports, or technical memoranda and is the coauthor of several books. He is a member of the IEEE and is the recipient of the IEEE Sid Fernbach Award and the first IEEE Medal of Excellence in Scalable Computing.

Inhaltsangabe

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.