accelerate-0.15.1.0: An embedded language for accelerated array processing

accelerate-0.15.1.0: An embedded language for accelerated array processing

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.CUDA it may be on-the-fly off-loaded to the GPU.

Available backends

Currently, there are two backends:

  1. An interpreter that serves as a reference implementation of the intended semantics of the language, which is included in this package.
  2. A CUDA backend generating code for CUDA-capable NVIDIA GPUs: http://hackage.haskell.org/package/accelerate-cuda

Several experimental and/or incomplete backends also exist. If you are particularly interested in any of these, especially with helping to finish them, please contact us.

  1. Cilk/ICC and OpenCL: https://github.com/AccelerateHS/accelerate-backend-kit
  2. Another OpenCL backend: https://github.com/HIPERFIT/accelerate-opencl
  3. A backend to the Repa array library: https://github.com/blambo/accelerate-repa
  4. An infrastructure for generating LLVM code, with backends targeting multicore CPUs and NVIDIA GPUs: https://github.com/AccelerateHS/accelerate-llvm/
Additional components

The following support packages are available:

  1. accelerate-cuda: A high-performance parallel backend targeting CUDA-enabled NVIDIA GPUs. Requires the NVIDIA CUDA SDK and, for full functionality, hardware with compute capability 1.1 or greater. See the table on Wikipedia for supported GPUs: http://en.wikipedia.org/wiki/CUDA#Supported_GPUs
  2. accelerate-examples: Computational kernels and applications showcasing Accelerate, as well as performance and regression tests.
  3. accelerate-io: Fast conversion between Accelerate arrays and other formats, including vector and repa.
  4. accelerate-fft: Computation of Discrete Fourier Transforms.

Install them from Hackage with cabal install PACKAGE

Examples and documentation

Haddock documentation is included in the package, and a tutorial is available on the GitHub wiki: https://github.com/AccelerateHS/accelerate/wiki

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
  • A cellular automata simulation
  • A "password recovery" tool, for dictionary lookup of MD5 hashes
  • A simple interactive ray tracer
Mailing list and contacts
Hackage note

The module documentation list generated by Hackage is incorrect. The only exposed modules should be:

Modules