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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
Languages
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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 FREE CHAPTER 2. Choosing the Correct Data Structures 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

Summary


In this chapter we have imported C functions as Haskell functions, exported Haskell functions as C functions, passed pointers (both foreign and stable) and data through the FFI, built a shared library with Haskell, and used hsc2hsto to write a Storable instance for a custom datatype. You have learned to invoke the FFI from both the C and the Haskell side and to manage memory in both the Haskell heap and the lower-level memory area also used by C.

The next chapter will be about another implementation-level concept like the FFI: GPU-programming using Haskell. Graphics processors are much better suited for highly parallel number-crunching applications, which is the reason for the GPU's popularity in high-performance numeric computing. An excellent Haskell library, Accelerate, defines a language that greatly simplifies usually mundane and hard GPU programming. In addition, Accelerate is backend-agnostic: the same code could run on any hardware solution (CPU/LLVM, CUDA, OpenCL, and others...

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