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:
An interpreter that serves as a reference implementation of the intended
semantics of the language, which is included in this package.
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.
Cilk/ICC and OpenCL: https://github.com/AccelerateHS/accelerate-backend-kit
Another OpenCL backend: https://github.com/HIPERFIT/accelerate-opencl
A backend to the Repa array library: https://github.com/blambo/accelerate-repa
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:
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
accelerate-examples: Computational kernels and applications showcasing
Accelerate, as well as performance and regression tests.
accelerate-io: Fast conversion between Accelerate arrays and other
formats, including vector and repa.
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:
