Is Parallel Programming Hard,…

Is Parallel Programming Hard, And, If So, What Can You Do About It? by Paul E. McKenney.

From Chapter 1 How To Use This Book:

The purpose of this book is to help you understand how to program shared-memory parallel machines without risking your sanity.[1] By describing the algorithms and designs that have worked well in the past, we hope to help you avoid at least some of the pitfalls that have beset parallel-programming projects. But you should think of this book as a foundation on which to build, rather than as a completed cathedral. Your mission, if you choose to accept, is to help make further progress in the exciting field of parallel programming—progress that should in time render this book obsolete. Parallel programming is not as hard as some say, and we hope that this book makes your parallel-programming projects easier and more fun.

In short, where parallel programming once focused on science, research, and grand-challenge projects, it is quickly becoming an engineering discipline. We therefore examine the specific tasks required for parallel programming and describe how they may be most effectively handled. In some surprisingly common special cases, they can even be automated.

This book is written in the hope that presenting the engineering discipline underlying successful parallel-programming projects will free a new generation of parallel hackers from the need to slowly and painstakingly reinvent old wheels, enabling them to instead focus their energy and creativity on new frontiers. We sincerely hope that parallel programming brings you at least as much fun, excitement, and challenge that it has brought to us!

I should not have been surprised by:

16.4 Functional Programming for Parallelism

When I took my first-ever functional-programming class in the early 1980s, the professor asserted that the side- effect-free functional-programming style was well-suited to trivial parallelization and analysis. Thirty years later, this assertion remains, but mainstream production use of parallel functional languages is minimal, a state of affairs that might well stem from this professor’s additional assertion that programs should neither maintain state nor do I/O. There is niche use of functional languages such as Erlang, and multithreaded support has been added to several other functional languages, but mainstream production usage remains the province of procedural languages such as C, C++, Java, and FORTRAN (usually augmented with OpenMP or MPI).

The state of software vulnerability is testimony enough to the predominance of C, C++, and Java.

I’m not real sure I would characterize Erlang as a “niche” language. Niche languages aren’t often found running telecommunications networks, or at least that is my impression.

I would take McKenney’s comments as a challenge to use functional languages such as Clojure and Erlang to make in-roads into mainstream production.

While you use this work to learn the procedural approach to parallelism, you can be building contrasts to a functional one.

I first saw this in Nat Torkington’s Four short links: 13 March 2014.

Comments are closed.