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Haskell High Performance Programming

You're reading from   Haskell High Performance Programming Write Haskell programs that are robust and fast enough to stand up to the needs of today

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781786464217
Length 408 pages
Edition 1st Edition
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Author (1):
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Samuli Thomasson Samuli Thomasson
Author Profile Icon Samuli Thomasson
Samuli Thomasson
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Table of Contents (16) Chapters Close

Preface 1. Identifying Bottlenecks 2. Choosing the Correct Data Structures FREE CHAPTER 3. Profile and Benchmark to Your Heart's Content 4. The Devil's in the Detail 5. Parallelize for Performance 6. I/O and Streaming 7. Concurrency and Performance 8. Tweaking the Compiler and Runtime System (GHC) 9. GHC Internals and Code Generation 10. Foreign Function Interface 11. Programming for the GPU with Accelerate 12. Scaling to the Cloud with Cloud Haskell 13. Functional Reactive Programming 14. Library Recommendations 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...

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