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An Embedded Language for Accelerated Array Computations

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 recent paper Accelerating Haskell Array Codes with Multicore GPUs. There are also some slightly outdated slides and a video of a talk that I gave at the Haskell Implementors Workshop 2009 (in Edinburgh): Haskell Arrays, Accelerated (Using GPUs).

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 Data.Array.Accelerate.CUDA.run it may be on-the-fly off-loaded to a GPU.

Availability

Package accelerate is available from

  • Hackage: accelerate — install with cabal install accelerate
  • GitHub: mchakravarty/accelerate - get the source with git clone https://github.com/mchakravarty/accelerate.git

Requirements

  • Glasgow Haskell Compiler (GHC), 6.12.1 or later
  • Haskell libraries as specified in accelerate.cabal
  • For the CUDA backend, CUDA version 3.0 or later

Contacts

The maintainer of this package is Manuel M T Chakravarty chak@cse.unsw.edu.au (aka TacticalGrace on #haskell and related channels).

Both user and developer questions and discussions are welcome at accelerate@projects.haskell.org. Sorry, this mailing list is temporarily unavailable.

What's missing?

Here is a list of features that are currently missing:

  • Reification of sharing in scalar expressions (sharing is being recovered for array computations)
  • The CUDA backend does not support arrays of type Char and Bool at the moment.
  • The CUDA backend does not implement stencil computations yet.
  • Preliminary API (the current functionality is limited)

Documentation

Haddock documentation is included in the package and linked from the Hackage page. Furthermore, the source package contains a few simple examples in the accelerate-examples/ directory.

The idea behind the HOAS (higher-order abstract syntax) to de-Bruijn conversion used in the library is described separately.