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

Mathematics, statistics, and science


The libraries in this subsection are as follows:

  • hmatrix: Highish-level library for doing linear algebra in Haskell using BLAS and LAPACK under the hood.

  • hmatrix-gsl-stats: Bindings to GSL, based on hmatrix.

  • hstatistics: Some statistical functions built on top of hmatrix and hmatrix-gsl-stats.

  • statistics: Pure Haskell statistics functions. Focuses on high performance and robustness.

  • Frames: Working with CSV and other tabular data sets so large that they don't always fit in memory.

  • matrix: A fairly efficient matrix datatype in pure Haskell, with basic matrix operations.

For linear algebra and statistics, there are a few useful packages. The hmatrix/hmatrix-gsl-stats/hstatistics provide pretty good bindings to well-known BLAS, LAPACK, and GSL libraries. The statistics package is very different, being a pure-Haskell implementation of a variety of statistics utilities.

Working with large datasets in Haskell is made easy with Frames. It provides a type-safe data frame...