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:
 Accelerating Haskell Array Codes with Multicore GPUs
 Optimising Purely Functional GPU Programs (slides)
 Embedding Foreign Code
 Typesafe Runtime Code Generation: Accelerate to LLVM (slides) (video)
 Streaming Irregular Arrays (video)
There are also slides from some fairly recent presentations:
 Embedded Languages for HighPerformance Computing in Haskell
 GPGPU Programming in Haskell with Accelerate (video) (workshop)
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:
 acceleratellvmnative: Backend targeting multicore CPUs
 acceleratellvmptx: Backend targeting CUDAenabled NVIDIA GPUs. Requires a GPU with compute capability 2.0 or greater (see the table on Wikipedia)
 accelerateexamples: Computational kernels and applications showcasing the use of Accelerate as well as a regression test suite (supporting function and performance testing)
 Conversion between various formats:
 accelerateio: For copying data directly between raw pointers
 accelerateioarray: Immutable arrays
 accelerateiobmp: Uncompressed BMP image files
 accelerateiobytestring: Compact, immutable binary data
 accelerateiocereal: Binary serialisation of arrays using cereal
 accelerateioJuicyPixels: Images in various pixel formats
 accelerateiorepa: Another Haskell library for highperformance parallel arrays
 accelerateioserialise: Binary serialisation of arrays using serialise
 accelerateiovector: Efficient boxed and unboxed onedimensional arrays
 acceleratefft: Fast Fourier transform implementation, with FFI bindings to optimised implementations
 accelerateblas: BLAS and LAPACK operations, with FFI bindings to optimised implementations
 acceleratebignum: Fixedwidth large integer arithmetic
 colouraccelerate: Colour representations in Accelerate (RGB, sRGB, HSV, and HSL)
 containersaccelerate: Hashingbased container types
 glossaccelerate: Generate gloss pictures from Accelerate
 glossrasteraccelerate: Parallel rendering of raster images and animations
 hashableaccelerate: A class for types which can be converted into a hash value
 lensaccelerate: Lens operators for Accelerate types
 linearaccelerate: Linear vector spaces in Accelerate
 mwcrandomaccelerate: Generate Accelerate arrays filled with high quality pseudorandom numbers
 numericpreludeaccelerate: Lifting the numericprelude to Accelerate
 wignervilleaccelerate: WignerVille timefrequency distribution.
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.
Additional examples
Accelerate users have also built some substantial applications of their own. Please feel free to add your own examples!
 Jonathan Fraser, GPUVAC: An explicit advection magnetohydrodynamics simulation
 David van Balen, Sudokus: A sudoku solver
 Trevor L. McDonell, lolaccelerate: A backend to the Λ ○ λ (Lol) library for ringbased lattice cryptography
 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)
Who are we?
The Accelerate team (past and present) consists of:
 Manuel M T Chakravarty (@mchakravarty)
 Gabriele Keller (@gckeller)
 Trevor L. McDonell (@tmcdonell)
 Robert CliftonEverest (@robeverest)
 Frederik M. Madsen (@fmma)
 Ryan R. Newton (@rrnewton)
 Joshua Meredith (@JoshMeredith)
 Ben Lever (@blever)
 Sean Seefried (@sseefried)
 Ivo Gabe de Wolff (@ivogabe)
The maintainer and principal developer of Accelerate is Trevor L. McDonell trevor.mcdonell@gmail.com.
Mailing list and contacts
 Mailing list:
acceleratehaskell@googlegroups.com
(discussions on both use and development are welcome)  Sign up for the mailing list at the Accelerate Google Groups page
 Bug reports and issues tracking: GitHub project page
 Chat with us on gitter
Citing Accelerate
If you use Accelerate for academic research, you are encouraged (though not required) to cite the following papers:

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.

Robert CliftonEverest, Trevor L. McDonell, Manuel M. T. Chakravarty, and Gabriele Keller. Streaming Irregular Arrays. In Haskell '17: The 10th ACM SIGPLAN Symposium on Haskell, ACM, 2017.
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)
 Many more features... contact us!