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Haskell High Performance Programming

Haskell High Performance Programming

By : Thomasson
3 (2)
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Haskell High Performance Programming

Haskell High Performance Programming

3 (2)
By: 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 (16 chapters)
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15
Index

What you need for this book

To run most examples in this book, all you need is a working, relatively recent, installation of GHC and some Haskell libraries. Examples are built for nix-like systems, although they are easily adapted for a Windows machine.

The recommended minimum version for GHC is 7.6. The Haskell libraries needed are introduced in the chapters in which they are used. In Chapter 4, The Devil's in the Detail, we use the Haskell Stack tool to perform some tasks, but it isn't strictly required, although it is recommended to install Stack.

In Chapter 11, Programming for the GPU Using Accelerate, executing the CUDA versions of examples requires a CUDA-enabled system and the installation of the CUDA platform.

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