Module documentation for 0.15.1.0
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
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
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
Mailing list: [email protected] (discussion of both use and development welcome).
Sign up for the mailing list here: http://groups.google.com/group/accelerate-haskell
Bug reports and issue tracking: https://github.com/AccelerateHS/accelerate/issues
- Hackage note
The module documentation list generated by Hackage is incorrect. The only exposed modules should be:
Compiles with ghc-7.8 and ghc-7.10
Minor bug fixes
- 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