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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

Running with the CUDA backend

To compile using the CUDA backend, we should install the accelerate-cuda package from Hackage. Also required is the CUDA platform. Refer to the accelerate-cuda package documentation and CUDA platform documentation for further information:

cabal install accelerate-cuda -fdebug

The Haskell dependencies require some additional tools in scope, including alex, happy, and c2hs. Install those first if necessary. The debug flag gives our Accelerate CUDA programs some additional tools. There's no extra runtime cost versus no debug flag. The additional flags could interfere with the user program, though.

In principle, the only necessary code change for using the CUDA backend instead of the interpreter is to import the run function from Data.Array.Accelerate.CUDA instead of the Interpreter module:

import Data.Array.Accelerate.CUDA

The program below executes our matrix product of 100x100 matrices on the GPU using CUDA. Note that swapping back to the interpreter is a...

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