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An Embedded Language for Accelerated Array Computations

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Data.Array.Accelerate defines an embedded language of array computations for high-performance computing in Haskell. Computations on multi-dimensional, regular arrays are expressed in the form of parameterised collective operations (such as maps, reductions, and permutations). These computations are online-compiled and executed on a range of architectures.

For more details, see our papers:

There are also slides from some fairly recent presentations:

Chapter 6 of Simon Marlow's book Parallel and Concurrent Programming in Haskell contains a tutorial introduction to Accelerate.

Trevor's PhD thesis details the design and implementation of frontend optimisations and CUDA backend.

Table of Contents

A simple example

As a simple example, consider the computation of a dot product of two vectors of single-precision floating-point numbers:

dotp :: Acc (Vector Float) -> Acc (Vector Float) -> Acc (Scalar Float)
dotp xs ys = fold (+) 0 (zipWith (*) xs ys)

Except for the type, this code is almost the same as the corresponding Haskell code on lists of floats. The types indicate that the computation may be online-compiled for performance — for example, using it may be on-the-fly off-loaded to a GPU.


Package accelerate is available from

  • Hackage: accelerate — install with cabal install accelerate
  • GitHub: AccelerateHS/accelerate - get the source with git clone

Additional components

The following supported addons are available as separate packages on Hackage and included as submodules in the GitHub repository:

  • accelerate-cuda Backend targeting CUDA-enabled NVIDA GPUs — requires the NVIDIA CUDA SDK and hardware with compute capability 1.2 or greater (see the table on Wikipedia)
  • accelerate-examples Computational kernels and applications showcasing the use of Accelerate as well as a regression test suite (supporting function and performance testing)
  • accelerate-io Fast conversion between Accelerate arrays and other array formats (including Repa arrays)
  • accelerate-fft Fast Fourier transform implementation, with optimised implementation for the CUDA backend

Install them from Hackage with cabal install PACKAGENAME.

The following components are experimental and/or incomplete:

  • accelerate-llvm A framework for constructing backends targeting LLVM IR, with concrete backends for multicore CPUs and NVIDIA GPUs.
  • accelerate-backend-kit Simplified internal AST to get going on writing backends

The following components are incomplete or not currently maintained. Please contact us if you are interested in working on them!


  • Glasgow Haskell Compiler (GHC), 7.8.3 or later
  • For the CUDA backend, CUDA version 5.0 or later
  • Haskell libraries as specified in the accelerate.cabal and optionally accelerate-cuda.cabal files.


  • Haddock documentation is included in the package and linked from the Hackage page.
  • Online documentation is on the GitHub wiki.
  • The idea behind the HOAS (higher-order abstract syntax) to de-Bruijn conversion used in the library is described separately.


The GitHub repository contains a submodule accelerate-examples, which provides a range of computational kernels and a few complete applications. To install these from Hackage, issue cabal install accelerate-examples. The examples include:

  • An implementation of canny edge detection
  • An interactive mandelbrot set generator
  • An N-body simulation of gravitational attraction between solid particles
  • An implementation of the PageRank algorithm
  • A simple ray-tracer
  • A particle based simulation of stable fluid flows
  • A cellular automata simulation
  • A "password recovery" tool, for dictionary lookup of MD5 hashes

Mandelbrot Raytracer

Accelerate users have also built some substantial applications of their own. Please feel free to add your own examples!

  • Henning Thielemann, patch-image: Combine a collage of overlapping images
  • apunktbau, bildpunkt: A ray-marching distance field renderer
  • klarh, hasdy: Molecular dynamics in Haskell using Accelerate
  • Alexandros Gremm used Accelerate as part of the 2014 CSCS summer school (code)

Mailing list and contacts

The maintainers of Accelerate are Manuel M T Chakravarty and Trevor L McDonell

Citing Accelerate

If you use Accelerate for academic research, you are encouraged (though not required) to cite the following papers (BibTeX):

  • Manuel M. T. Chakravarty, Gabriele Keller, Sean Lee, Trevor L. McDonell, and Vinod Grover. Accelerating Haskell Array Codes with Multicore GPUs. In DAMP '11: Declarative Aspects of Multicore Programming, ACM, 2011.

  • Trevor L. McDonell, Manuel M. T. Chakravarty, Gabriele Keller, and Ben Lippmeier. Optimising Purely Functional GPU Programs. In ICFP '13: The 18th ACM SIGPLAN International Conference on Functional Programming, ACM, 2013.

  • Robert Clifton-Everest, Trevor L. McDonell, Manuel M. T. Chakravarty, and Gabriele Keller. Embedding Foreign Code. In PADL '14: The 16th International Symposium on Practical Aspects of Declarative Languages, Springer-Verlag, LNCS, 2014.

  • Trevor L. McDonell, Manuel M. T. Chakravarty, Vinod Grover, and Ryan R. Newton. Type-safe Runtime Code Generation: Accelerate to LLVM. In Haskell '15: The 8th ACM SIGPLAN Symposium on Haskell, ACM, 2015.

Accelerate is primarily developed by academics, so citations matter a lot to us. As an added benefit, you increase Accelerate's exposure and potential user (and developer!) base, which is a benefit to all users of Accelerate. Thanks in advance!

What's missing?

Here is a list of features that are currently missing:

  • Preliminary API (parts of the API may still change in subsequent releases)