Parallel MATLAB for Multicore and Multinode Computers

Parallel MATLAB for Multicore and Multinode Computers

Hardback Software, Environments and Tools

By (author) Jeremy Kepner

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  • Publisher: Society for Industrial & Applied Mathematics,U.S.
  • Format: Hardback | 273 pages
  • Dimensions: 183mm x 259mm x 20mm | 680g
  • Publication date: 31 August 2009
  • Publication City/Country: New York
  • ISBN 10: 089871673X
  • ISBN 13: 9780898716733
  • Edition: 1
  • Sales rank: 1,216,000

Product description

This is the first book on parallel MATLAB and the first parallel computing book focused on the design, code, debug, and test techniques required to quickly produce efficient parallel programs. MATLAB is currently the dominant language of technical computing with one million users worldwide, many of whom can benefit from the increased power offered by inexpensive multicore and multinode parallel computers. MATLAB is an ideal environment for learning about parallel computing, allowing the user to focus on parallel algorithms instead of the details of implementation. This book is a reference for professional scientists and engineers and will also be beneficial to graduate students and advanced undergraduate students as a textbook for a parallel computing course.

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Author information

Jeremy Kepner is a senior technical staff member at MIT Lincoln Laboratory. His research focuses on the development of advanced libraries for the application of massively parallel computing to a variety of data intensive signal processing problems on which he has published many articles. Jeremy is most proud of the opportunity he has had to be the principal architect, principal investigator, or otherwise co-leader of several very talented teams. These teams have produced a number of innovative technologies that have broken new ground in parallel computing.

Table of contents

List of figures; List of tables; List of algorithms; Preface; Acknowledgments; Part I. Fundamentals: 1. Primer: notation and interfaces; 2. Introduction to pMATLAB; 3. Interacting with distributed arrays; Part II. Advanced Techniques: 4. Parallel programming models; 5. Advanced distributed array programming; 6. Performance metrics and software architecture; Part III. Case Studies: 7. Parallel application analysis; 8. Stream; 9. RandomAccess; 10. Fast Fourier transform; 11. High performance Linpack; Appendix. Notation for hierarchical parallel multicore algorithms; Index.