Principles of Parallel Programming / Edition 1

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With the rise of multi-core architecture, parallel programming is an increasingly important topic for software engineers and computer system designers. Written by well-known researchers Larry Snyder and Calvin Lin, this highly anticipated first edition emphasizes the principles underlying parallel computation, explains the various phenomena, and clarifies why these phenomena represent opportunities or barriers to successful parallel programming. Ideal for an advanced upper-level undergraduate course, Principles of Parallel Programming supplies enduring knowledge that will outlive the current hardware and software, aiming to inspire future researchers to build tomorrow’s solutions.
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Editorial Reviews

From the Publisher

"...the first basic book on the subject that I've ever seen that seems to have the pulse on the true issues of parallelism that are relevant for students."Alan Edelman, MIT

"Principles of Parallel Programming is a wonderful book and I plan to use it in our new parallel programming course..."Peiyi Tang, University of Arkansas, Little Rock

"I like [Principles of Parallel Programming] very much for a few specific reasons: it's concise, covers the most relevant topics but does not take thousand pages to do it, it is hands on and it covers...recent developments with multi-core and GPGPU."Edin Hodzic, Santa Clara University

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Product Details

  • ISBN-13: 9780321487902
  • Publisher: Pearson
  • Publication date: 3/11/2008
  • Series: Alternative eText Formats Series
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 352
  • Sales rank: 866,133
  • Product dimensions: 7.40 (w) x 9.20 (h) x 0.90 (d)

Meet the Author

Lawrence Snyder is Professor of Computer Science and Engineering at the University of Washington in Seattle. He received his PhD from Carnegie Mellon University and has devoted most of his career to parallel computation research, including architecture, algorithms and languages. With Calvin Lin and UW graduate students, he developed the ZPL parallel programming language. He is a fellow of the ACM and IEEE. He is an ardent traveler, enthusiastic theater-goer and occasional skier.

Calvin Lin is an Associate Professor of Computer Sciences at The University of Texas at Austin, where he also serves as Director of the Turing Scholars Honors Program of undergraduate CS majors. He received his PhD from the University of Washington under the supervision of Lawrence Snyder. His current research interests include compilers and micro-architecture. In his spare time, he is an avid ultimate Frisbee player and coach of UT’s Men’s Ultimate Frisbee team.

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Table of Contents

Chapter 1 Introduction: Parallelism = Opportunities + Challenges
The Power and Potential of Parallelism
Examining Sequential and Parallel Programs
A Paradigm Shift
Parallelism Using Multiple Instruction Streams
The Goals: Scalable Performance and Portability
Historical Context

Chapter 2 Parallel Computers And Their Model
Balancing Machine Specifics with Portability
A Look at Five Parallel Computers
The RAM: An Abstraction of a Sequential Computer
The PRAM: A Parallel Computer Model
The CTA: A Practical Parallel Computer Model
Memory Reference Mechanisms
A Closer Look at Communication
Applying the CTA Model
Historical Perspective

Chapter 3 Reasoning about Performance
Motivation and Some Basic Concepts
Sources of Performance Loss
Parallel Structure
Reasoning about Performance
Performance Trade-Offs
Measuring Performance
What should we measure?
Historical Perspective

Chapter 4 First Steps Towards Parallel Programming
Task and Data Parallelism
Count 3s Example
Conceptualizing Parallelism
Alphabetizing Example
Comparison of Three Solutions
Historical Perspective

Chapter 5 Scalable Algorithmic Techniques
The Inevitability of Trees
Blocks of Independent Computation
Schwartz’ Algorithm
Assigning Work To Processes Statically
Assigning Work to Processes Dynamically
The Reduce & Scan Abstractions
Historical Context

Chapter 6 Programming with Threads
POSIX Threads
Thread Creation and Destruction
Mutual Exclusion
Safety Issues
Performance Issues
Open MP
The Count 3s Example
Semantic Limitations on
Thread Behavior and Interaction
Summary of OpenMP
Java Threads
Historical Perspectives

Chapter 7 Local View Programming Languages
MPI: The Message Passing Interface
Getting Started
Safety Issues
Performance Issues
Co-Array Fortran
Unified Parallel C

Chapter 8 Global View Programming Languages
The Z-level Programming Language
Basic Concepts of ZPL
Life, An Example
Design Principles
Manipulating Arrays Of Different Ranks
Reordering Data With Remap
Parallel Execution of ZPL
Performance Model
Historical Context

Chapter 9 Assessing Our Knowledge
Evaluating Existing Approaches
Lessons for the Future
Historical Perspectives

Chapter 10 Future Directions in Parallel Programming
Attached Processors
Grid Computing
Transactional Memory

Chapter 11 Capstone Project: Designing a Parallel Program
Getting Started
Historical Perspective

Appendix 1 More Advanced Concepts

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Customer Reviews

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  • Posted February 10, 2009

    more from this reviewer

    abstracts essential ideas

    Years ago I briefly worked on a hypercube, and when I got this book, I wondered how it had fared. Alas, the hypercube, at least under this name, rated no mention. Though there is a passing reference to a binary 3 cube which is a 3 dimensional hypercube.<BR/><BR/>The authors explain the current state of multiprocessor architectures. The few remaining computer CPU makers have efforts in this field. Intel, AMD, Sun and IBM. The book describes qualitatively the salient aspects of each. One nice thing about the discussion is that it focuses on this, without drowning you in unnecessary hardware details. This turns out to be a key theme of the book. It abstracts out essential hardware properties, so that you can appreciate these and apply the book's ideas without being tied to any given chip.<BR/><BR/>The book also describes an important type of multiprocessor. Cluster machines, where each node is typically some off the shelf CPU, buffed up with a lot of local memory. The key differences between clusters are often related to how the nodes are hooked to each other, by some type of bus or crossbar. Affordability is an important property of clusters; thus the maximal use of commodity hardware. (The hypercube that I mentioned earlier would be a cluster.)<BR/><BR/>For a programmer, there is one overriding idea that you should get from the book. For optimal performance, minimise the internodal communication, compared to the use of a node's cache. The access time of the former can be 2-5 orders of magnitude slower. Details vary with the given architectures, of course. But typically nothing else comes close, in terms of effects on your throughput.

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