Programming on Parallel Machines

Why is this book different from all other parallel programming books? It is aimed more on the practical end of things, in that:

• There is very little theoretical content, such as O() analysis, maximum theoretical speedup, PRAMs, directed acyclic graphs (DAGs) and so on.
• Real code is featured throughout.
• We use the main parallel platforms - OpenMP, CUDA and MPI - rather than languages that at this stage are largely experimental or arcane.
• The running performance themes - communications latency, memory/network contention, load balancing and so on - are interleaved throughout the book, discussed in the context of specific platforms or applications.
• Considerable attention is paid to techniques for debugging.

The main programming language used is C/C++, but some of the code is in R, which has become the pre-eminent language for data analysis. As a scripting language, R can be used for rapid prototyping. In our case here, it enables me to write examples which are much less less cluttered than they would be in C/C++, thus easier for students to discern the fundamental parallelixation principles involved. For the same reason, it makes it easier for students to write their own parallel code, focusing on those principles. And R has a rich set of parallel libraries.

It is assumed that the student is reasonably adept in programming, and has math background through linear algebra. An appendix reviews the parts of the latter needed for this book. Another appendix presents an overview of various systems issues that arise, such as process scheduling and virtual memory.

It should be note that most of the code examples in the book are NOT optimized. The primary emphasis is on simplicity and clarity of the techniques and languages used. However, there is plenty of discussion on factors that affect speed, such cache coherency issues, network delays, GPU memory structures and so on.

Here's how to get the code files you'll see in this book: The book is set in LaTeX, and the raw .tex files are available in http://heather.cs.ucdavis.edu/~matloff/158/PLN. Simply download the relevant file (the file names should be clear), then use a text editor to trim to the program code of interest.

In order to illustrate for students the fact that research and teaching (should) enhance each other, I occasionally will make brief references here to some of my research work.

Like all my open source textbooks, this one is constantly evolving. I continue to add new topics, new examples and so on, and of course fix bugs and improve the exposition. For that reason, it is better to link to the latest version, which will always be at http://heather.cs.ucdavis.edu/~matloff/158/PLN/ParProcBook.pdf, rather than to copy it.

For that reason, feedback is highly appreciated. I wish to thank Stuart Ambler, Matt Butner, Stuart Hansen, Bill Hsu, Sameer Khan, Mikel McDaniel, Richard Minner, Lars Seeman and Johan Wikstr'om for their comments. I'm also very grateful to Professor Hsu for his making available to me advanced GPU-equipped machines.