An Embedded Language for Accelerated Array Computations
Data.Array.Accelerate
defines an embedded language of array computations for highperformance computing in Haskell. Computations on multidimensional, regular arrays are expressed in the form of parameterised collective operations (such as maps, reductions, and permutations). These computations are onlinecompiled 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 singleprecision floatingpoint 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 onlinecompiled for performance; for example, using Data.Array.Accelerate.LLVM.PTX.run
it may be onthefly offloaded to a GPU.
Availability
Package accelerate is available from
 Hackage: accelerate  install with
cabal install accelerate
 GitHub: AccelerateHS/accelerate  get the source with
git clone https://github.com/AccelerateHS/accelerate.git
. The easiest way to compile the source distributions is via the Haskell stack tool.
Additional components
The following supported addons are available as separate packages:
Install them from Hackage with cabal install PACKAGENAME
.
Documentation
 Haddock documentation is included and linked with the individual package releases on Hackage.
 Haddock documentation for indevelopment components can be found here.
 The idea behind the HOAS (higherorder abstract syntax) to deBruijn conversion used in the library is described separately.
Examples
accelerateexamples
The accelerateexamples package provides a range of computational kernels and a few complete applications. To install these from Hackage, issue cabal install accelerateexamples
. The examples include:
 An implementation of canny edge detection
 An interactive mandelbrot set generator
 An Nbody simulation of gravitational attraction between solid particles
 An implementation of the PageRank algorithm
 A simple raytracer
 A particle based simulation of stable fluid flows
 A cellular automata simulation
 A “password recovery” tool, for dictionary lookup of MD5 hashes
LULESH
LULESHaccelerate is in implementation of the Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics (LULESH) miniapp. LULESH represents a typical hydrodynamics code such as ALE3D, but is a highly simplified application, hardcoded to solve the Sedov blast problem on an unstructured hexahedron mesh.
Λ ○ λ (Lol)
Λ ○ λ (Lol) is a generalpurpose library for ringbased lattice cryptography. Lol has applications in, for example, symmetrickey somewhathomomorphic encryption schemes. The lolaccelerate package provides an Accelerate backend for Lol.
Additional examples
Accelerate users have also built some substantial applications of their own.
Please feel free to add your own examples!
 Henning Thielemann, patchimage: Combine a collage of overlapping images
 apunktbau, bildpunkt: A raymarching 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 chak@cse.unsw.edu.au and Trevor L McDonell tmcdonell@cse.unsw.edu.au.
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 CliftonEverest, 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, SpringerVerlag, LNCS, 2014.

Trevor L. McDonell, Manuel M. T. Chakravarty, Vinod Grover, and Ryan R. Newton.
Typesafe 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)