An Embedded Language for Accelerated Array Computations

Data.Array.Accelerate defines an embedded language of array computations for high-performance computing. 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, please have a look at our recent paper Accelerating Haskell Array Codes with Multicore GPUs. There are some slightly outdated slides and a video of a talk that I gave at the Haskell Implementors Workshop 2009 (in Edinburgh): Haskell Arrays, Accelerated (Using GPUs).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 is off-loaded to a GPU.

Availability

• Package accelerate is available from Hackage: accelerate.
• The main development page and source repository is on GitHub: accelerate @ GitHub

Mailing list

Both user and developer questions and discussions are welcome at accelerate-haskell@googlegroups.com (Google Groups page).

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.

Internals

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

The PLS Accelerate Team (alphabetically)

• Manuel Chakravarty, University of New South Wales
• Gabriele Keller, University of New South Wales
• Sean Lee, NVIDIA Corporation (formerly, University of New South Wales)
• Trevor McDonell, University of New South Wales

Further contributions by Ben Lever, Rami Mukhtar, and Sean Seefried.