Module documentation for 126.96.36.199
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.LLVM.PTX it may be on-the-fly off-loaded to the GPU.
- Additional components
The following supported add-ons are available as separate packages. Install
them from Hackage with
cabal install <package>
accelerate-llvm-native: Backend supporting parallel execution on multicore CPUs.
accelerate-llvm-ptx: Backend supporting parallel execution on CUDA-capable NVIDIA GPUs. Requires a GPU with compute capability 2.0 or greater. See the following table for supported GPUs: http://en.wikipedia.org/wiki/CUDA#Supported_GPUs
accelerate-cuda: Backend targeting CUDA-enabled NVIDIA GPUs. Requires a GPU with compute compatibility 1.2 or greater. /NOTE: This backend is being deprecated in favour of
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 conversions between Accelerate arrays and other array formats (including vector and repa).
accelerate-fft: Discrete Fourier transforms, with FFI bindings to optimised implementations.
accelerate-bignum: Fixed-width large integer arithmetic.
colour-accelerate: Colour representations in Accelerate (RGB, sRGB, HSV, and HSL).
gloss-accelerate: Generate gloss pictures from Accelerate.
gloss-raster-accelerate: Parallel rendering of raster images and animations.
lens-accelerate: Lens operators for Accelerate types.
linear-accelerate: Linear vector spaces in Accelerate.
mwc-random-accelerate: Generate Accelerate arrays filled with high quality pseudorandom numbers.
- Examples and documentation
Haddock documentation is included in the package
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
An implementation of the PageRank algorithm
A simple interactive ray tracer
A particle based simulation of stable fluid flows
A cellular automata simulation
A "password recovery" tool, for dictionary lookup of MD5 hashes
lulesh-accelerate is an implementation of the Livermore Unstructured
Lagrangian Explicit Shock Hydrodynamics (LULESH) mini-app. LULESH represents a
typical hydrodynamics code such as ALE3D, but is highly simplified and
hard-coded to solve the Sedov blast problem on an unstructured hexahedron
- Mailing list and contacts
Many API and internal changes
Bug fixes and other enhancements
- Bug fixes and performance improvements.
New iteration constructs.
Additional Prelude-like functions.
Improved code generation and fusion optimisation.
Concurrent kernel execution in the CUDA backend.
New array fusion optimisation.
New foreign function interface for array and scalar expressions.
Additional Prelude-like functions.
New example programs.
Bug fixes and performance improvements.
Full sharing recovery in scalar expressions and array computations.
Two new example applications in package
accelerate-examples: Real-time Canny edge detection and an interactive fluid flow simulator (both including a graphical frontend).
New Prelude-like functions
New simplified AST (in package
accelerate-backend-kit) for backend writers who want to avoid the complexities of the type-safe AST.
Complete sharing recovery for scalar expressions (but currently disabled by default).
Also bug fixes in array sharing recovery and a few new convenience functions.
Repa-style array indices
Additional collective operations supported by the CUDA backend:
Conversions to other array formats
- Bug fixes and some performance tweaks.
More collective operations supported by the CUDA backend:
foldSeg. Frontend and interpreter support for
- Initial release of the CUDA backend