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Embedded language for high-performance array computations

README.md

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 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.

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: AccelerateHS/accelerate - get the source with git clone https://github.com/AccelerateHS/accelerate.git

Additional components

The following supported addons are available as separate packages on Hackage and included as submodules in the GitHub repository:

  • accelerate-cuda Backend targeting CUDA-enabled NVIDA GPUs — requires the NVIDIA CUDA SDK and hardware with compute capability 1.2 or greater (see the table on Wikipedia)
  • 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 conversion between Accelerate arrays and other array formats (including Repa arrays)
  • accelerate-backend-kit Simplified internal AST to get going on writing backends
  • accelerate-buildbot Build bot for automatic performance & regression testing

Install them from Hackage with cabal install PACKAGENAME.

The following additional components are experimental and incomplete:

Requirements

  • Glasgow Haskell Compiler (GHC), 7.6 or later
  • For the CUDA backend, CUDA version 5.0 or later
  • Haskell libraries as specified in the accelerate.cabal and optionally accelerate-cuda.cabal files.

Documentation

  • Haddock documentation is included in the package and linked from the Hackage page.
  • Online documentation is on the GitHub wiki.
  • The idea behind the HOAS (higher-order abstract syntax) to de-Bruijn conversion used in the library is described separately.

Examples

The GitHub repository contains a submodule accelerate-examples, which provides a range of computational kernels and a few complete applications. To install these from Hackage, issue cabal install accelerate-examples. The examples include:

  • An implementation of canny edge detection
  • An interactive mandelbrot set generator
  • An N-body simulation of gravitational attraction between solid particles
  • An implementation of the PageRank algorithm
  • A simple ray-tracer
  • A particle based simulation of stable fluid flows
  • A cellular automata simulation
  • A "password recovery" tool, for dictionary lookup of MD5 hashes

Mandelbrot Raytracer

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.

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)
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