Structured Parallel Programming: Patterns for Efficient Computation

by Michael McCool, James Reinders, Arch Robison

Structured Parallel Programming offers the simplest way for developers to learn patterns for high-performance parallel programming. Written by parallel computing experts and industry insiders Michael McCool, Arch Robison, and James Reinders, this book explains how to design and implement maintainable and efficient parallel algorithms using a composable, structured, scalable, and machine-independent approach to parallel computing. It presents both theory and practice, and provides detailed concrete examples using multiple programming models. The examples in this book are presented using two of the most popular and cutting edge programming models for parallel Threading Building Blocks, and Cilk Plus. These architecture-independent models enable easy integration into existing applications, preserve investments in existing code, and speed the development of parallel applications. Examples from realistic contexts illustrate patterns and themes in parallel algorithm design that are widely applicable regardless of implementation technology. Software developers, computer programmers, and software architects will find this book extremely helpful.

Genres: Computer Science, Programming, Technical

432 pages, Paperback

First published June 27, 2012

Reviews

[Jörn Dinkla · Rating 4 out of 5]

Good if you program thread based on multi cores with shared memory with TBB or Cilk. Not much information if you use GPUs. Nothing on message based architectures. All-in-all it's good to read it. I learned from this book.

[Brian · Rating 4 out of 5]

If I was teach a parallel programming course, I might consider using this work (although I still have other, similar textbooks to review); however, were I to do so I would be confining my teaching to the first two parts and may even to just 1 parallel programming paradigm. Yes, I will admit that the last parallel programming course I took covered a diversity of paradigms (Cilk, vectorization, GPUs, OpenMP, MPI), yet I would have preferred to focus more on what one or two paradigms are capable of rather than just the taste of many. Parallel programming takes a lot of work to learn and this book is one piece in that effort.

Full review at: http://elegantc.blogspot.com/2015/04/...