Book Image

Haskell High Performance Programming

By : Samuli Thomasson
Book Image

Haskell High Performance Programming

By: Samuli Thomasson

Overview of this book

Haskell, with its power to optimize the code and its high performance, is a natural candidate for high performance programming. It is especially well suited to stacking abstractions high with a relatively low performance cost. This book addresses the challenges of writing efficient code with lazy evaluation and techniques often used to optimize the performance of Haskell programs. We open with an in-depth look at the evaluation of Haskell expressions and discuss optimization and benchmarking. You will learn to use parallelism and we'll explore the concept of streaming. We’ll demonstrate the benefits of running multithreaded and concurrent applications. Next we’ll guide you through various profiling tools that will help you identify performance issues in your program. We’ll end our journey by looking at GPGPU, Cloud and Functional Reactive Programming in Haskell. At the very end there is a catalogue of robust library recommendations with code samples. By the end of the book, you will be able to boost the performance of any app and prepare it to stand up to real-world punishment.
Table of Contents (21 chapters)
Haskell High Performance Programming
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Handling sequential data


The standard list,[], is the most used data structure for sequential data. It has reasonable performance, but when processing multiple small values, say Chars, the overhead of a linked list might be too much. Often, the convenient nature of [] is convincing enough.

The wide range of list functions in Data.List are hand-optimized and many are subject to fusion. List fusion, as it is currently implemented using the foldr/build fusion transformation, is subtly different from stream fusion employed in ByteString and Text (concatMap is a bit problematic with traditional stream fusion). Still, the end result is pretty much the same; in a long pipeline of list functions, intermediate lists will usually not be constructed.

Say we want a pipeline that first increases every element by one, calculates intermediate sums of all elements up to current element, and finally sums all elements. From the previous chapter, we have learned to write optimally strict recursive functions...