“Unlike almost every other book I’m aware of on parallel computing, you will not find a single example here dealing with solving partial differential equations and other applications of physics………” 

“While the book is chock full of examples, it aims to emphasize general principles. Accordingly, after presenting an introductory code example in Chapter 1 (general principles are meaningless without real examples to tie them to), I devote Chapter 2 not so much as how to write parallel code, as to explaining what the general factors are that can rob a parallel program of speed. Indeed, one can regard the entire book as addressing the plight of the poor guy described at the beginning of Chapter 2: 

Here is an all-too-common scenario:

 An analyst acquires a brand new multicore machine, capable of wondrous things. With great excitement, he codes up his favorite large problem on the new machine—only to find that the parallel version runs more slowly than the serial one. What a disappointment! Let’s see what factors can lead to such a situation… 

One thing this book is not, is a user manual. Though it uses specific tools throughout, such as R’s parallel and Rmpi libraries, OpenMP, CUDA and so on, this is for the sake of concreteness. The book will give the reader a solid introduction to these tools, but is not a compendium of all the different function arguments, environment options and so on. The intent is that the reader, upon completing this book, will be well-poised to learn more about these tools, and most importantly, to write effective parallel code in various other languages, be it Python, Julia or whatever.”