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

Representing data


Libraries for storing text and binary, and arbitrary data in different containers:

  • vector: High-performance fixed-size vectors with a powerful fusion framework. Supports unboxed (primitive) and boxed (arbitrary) elements with respective performance.

  • text: Fast, memory-efficient, and unicode-correct text datatypes. Both strict and lazy variants. Orders of magnitude faster than String in many use cases (not all).

  • bytestring: Extremely fast and efficient strict and lazy binary datatypes. Interfaces very well with the C FFI supporting marshalling in O(1).

  • containers: General-use immutable graph, map, set, sequence (a list with O(1) cons and snoc), tree structures for storing arbitrary (boxed) data.

  • unordered-containers: Efficient, immutable hash maps (tables) and sets.

  • hashtables: Efficient mutable hash maps and sets.

  • mutable-containers: A library that abstracts over multiple mutable variable and container types.

Refer to Chapter 2, Choose the Correct Data Structures for use and discussion...