An embedded language for accelerated array processing

Version on this page:
LTS Haskell 8.15:
Stackage Nightly 2017-05-28:
Latest on Hackage:
BSD3 licensed by Manuel M T Chakravarty, Robert Clifton-Everest, Gabriele Keller, Ben Lever, Trevor L. McDonell, Ryan Newtown, Sean Seefried
Maintained by Trevor L. McDonell

Module documentation for

There are no documented modules for this package.

Data.Array.Accelerate defines an embedded array language for 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 may then be online compiled and executed on a range of architectures.

A simple example

As a simple example, consider the computation of a dot product of two vectors of 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 Data.Array.Accelerate.LLVM.PTX it may be on-the-fly off-loaded to the GPU.

Additional components

The following supported add-ons are available as separate packages. Install them from Hackage with cabal install <package>

  • accelerate-llvm-native: Backend supporting parallel execution on multicore CPUs.

  • accelerate-llvm-ptx: Backend supporting parallel execution on CUDA-capable NVIDIA GPUs. Requires a GPU with compute capability 2.0 or greater. See the following table for supported GPUs:

  • accelerate-cuda: Backend targeting CUDA-enabled NVIDIA GPUs. Requires a GPU with compute compatibility 1.2 or greater. /NOTE: This backend is being deprecated in favour of accelerate-llvm-ptx./

  • 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 conversions between Accelerate arrays and other array formats (including vector and repa).

  • accelerate-fft: Discrete Fourier transforms, with FFI bindings to optimised implementations.

  • accelerate-bignum: Fixed-width large integer arithmetic.

  • colour-accelerate: Colour representations in Accelerate (RGB, sRGB, HSV, and HSL).

  • gloss-accelerate: Generate gloss pictures from Accelerate.

  • gloss-raster-accelerate: Parallel rendering of raster images and animations.

  • lens-accelerate: Lens operators for Accelerate types.

  • linear-accelerate: Linear vector spaces in Accelerate.

  • mwc-random-accelerate: Generate Accelerate arrays filled with high quality pseudorandom numbers.

Examples and documentation

Haddock documentation is included in the package

The accelerate-examples package demonstrates a range of computational kernels and several complete applications, including:

  • An implementation of the Canny edge detection algorithm

  • An interactive Mandelbrot set generator

  • A particle-based simulation of stable fluid flows

  • An n-body simulation of gravitational attraction between solid particles

  • An implementation of the PageRank algorithm

  • A simple interactive ray tracer

  • A particle based simulation of stable fluid flows

  • A cellular automata simulation

  • A "password recovery" tool, for dictionary lookup of MD5 hashes

lulesh-accelerate is an implementation of the Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics (LULESH) mini-app. LULESH represents a typical hydrodynamics code such as ALE3D, but is highly simplified and hard-coded to solve the Sedov blast problem on an unstructured hexahedron mesh.

Mailing list and contacts


  • Many API and internal changes

  • Bug fixes and other enhancements

  • Bug fixes and performance improvements.

  • New iteration constructs.

  • Additional Prelude-like functions.

  • Improved code generation and fusion optimisation.

  • Concurrent kernel execution in the CUDA backend.

  • Bug fixes.

  • New array fusion optimisation.

  • New foreign function interface for array and scalar expressions.

  • Additional Prelude-like functions.

  • New example programs.

  • Bug fixes and performance improvements.

  • Full sharing recovery in scalar expressions and array computations.

  • Two new example applications in package accelerate-examples: Real-time Canny edge detection and an interactive fluid flow simulator (both including a graphical frontend).

  • Bug fixes.

  • New Prelude-like functions zip*, unzip*, fill, enumFrom*, tail, init, drop, take, slit, gather*, scatter*, and shapeSize.

  • New simplified AST (in package accelerate-backend-kit) for backend writers who want to avoid the complexities of the type-safe AST.

  • Complete sharing recovery for scalar expressions (but currently disabled by default).

  • Also bug fixes in array sharing recovery and a few new convenience functions.

  • Streaming computations

  • Precompilation

  • Repa-style array indices

  • Additional collective operations supported by the CUDA backend: stencils, more scans, rank-polymorphic fold, generate.

  • Conversions to other array formats

  • Bug fixes

  • Bug fixes and some performance tweaks.

  • More collective operations supported by the CUDA backend: replicate, slice and foldSeg. Frontend and interpreter support for stencil.

  • Bug fixes.

  • Initial release of the CUDA backend

comments powered byDisqus