Handbook of Parallel Computing: Models, Algorithms and Applications (Chapman & Hall/CRC Computer & Information Science Series) [Hardcover]

Sanguthevar Rajasekaran (Editor), John Reif (Editor)

Book Description

ISBN-10: 1584886234 | ISBN-13: 978-1584886235 | Publication Date: December 20, 2007 | Edition: 1

The ability of parallel computing to process large data sets and handle time-consuming operations has resulted in unprecedented advances in biological and scientific computing, modeling, and simulations. Exploring these recent developments, the Handbook of Parallel Computing: Models, Algorithms, and Applications provides comprehensive coverage on all aspects of this field.

The first section of the book describes parallel models. It covers evolving computational systems, the decomposable bulk synchronous model, parallel random access machine-on-chip architecture, the parallel disks model, mobile agents, fault-tolerant computing, hierarchical performance modeling, the partitioned optical passive star network, and the reconfigurable mesh model. The subsequent section on parallel algorithms examines networks of workstations, grid and packet scheduling, the derandomization technique, isosurface extraction and rendering, suffix trees, and mobile computing algorithmics. The final part of the text highlights an array of problems and offers ways to combat these challenges.

This volume provides an up-to-date assessment of the models and algorithms involved in applying parallel computing to a variety of fields, from computational biology to wireless networking.

Editorial Reviews

About the Author

University of Connecticut, Storrs, USA Duke University, Durham, North Carolina, USA

Product Details

• Hardcover: 1224 pages
• Publisher: Chapman and Hall/CRC; 1 edition (December 20, 2007)
• Language: English
• ISBN-10: 1584886234
• ISBN-13: 978-1584886235
• Product Dimensions: 10.3 x 7.4 x 2.4 inches
• Shipping Weight: 4.8 pounds (View shipping rates and policies)
• Average Customer Review: 3.0 out of 5 stars  See all reviews (2 customer reviews)
• Amazon Best Sellers Rank: #2,287,184 in Books (See Top 100 in Books)

More About the Author

Discover books, learn about writers, read author blogs, and more.

Customer Reviews

Most Helpful Customer Reviews

2 of 2 people found the following review helpful:

2.0 out of 5 stars Not a handbook, November 18, 2008

By 

Antti Huima (California, USA) - See all my reviews
(REAL NAME)   

This review is from: Handbook of Parallel Computing: Models, Algorithms and Applications (Chapman & Hall/CRC Computer & Information Science Series) (Hardcover)

This book is not a handbook in any traditional sense. Unfortunately, it feels more like a collection of articles by people who wanted to publish their own research in an overly pompous context. There is lots of hand-waving in the book, the authors write in the book things like "our initial [and only] experiment shows Y" and "we plan to do X". The book should have been titled something like "Contemporary Issues in Parallel Computing" instead. And the text is sloppy. If you want to read about what those guys the editors invited to contribute were researching in 2008, fine, but if you search for a good account of the best practices, established theories and academic consensus in parallel computing today, do not buy this book. Compared e.g. to the Handbook of Theoretical Computer Science, this book is a joke.

1 of 1 people found the following review helpful:

4.0 out of 5 stars hardware and problems suited for that hardware, June 8, 2008

By 

W Boudville (Terra, Sol 3) - See all my reviews
(VINE VOICE)    (TOP 1000 REVIEWER)    (HALL OF FAME REVIEWER)    (REAL NAME)   

This review is from: Handbook of Parallel Computing: Models, Algorithms and Applications (Chapman & Hall/CRC Computer & Information Science Series) (Hardcover)

This CRC handbook gives us an impressive summary of the ideas in parallel computing. It describes the numerous hardware architectures that are currently in use. There is a wide variety of ways to bring many CPUs to bear on a large computational problem. The text also explains why parallel computing may be more widely used in future. It appears that the maximum performance of a single CPU is maxing out. Due in no small part to heat problems at gigahertz frequencies of operation.

So for future performance gains, being able to efficiently use several CPUs to decompose a problem might be necessary. For each architecture, you can get some idea of how to do this decomposition. From a reading, it is clear that not every problem will yield to parallel computing. But several classes of problems do.