accelerate

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

• 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

Release notes

• 0.15.0.0: Bug fixes and performance improvements.

• 0.14.0.0: New iteration constructs. Additional Prelude-like functions. Improved code generation and fusion optimisation. Concurrent kernel execution. Bug fixes.

• 0.13.0.0: 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.

• 0.12.0.0: Full sharing recovery in scalar expressions and array computations. Two new example applications in package accelerate-examples: Real-time Canny edge detection and fluid flow simulator (both including a graphical frontend). Bug fixes.

• 0.11.0.0: New Prelude-like functions zip*, unzip*, fill, enumFrom*, tail, init, drop, take, slit, gather*, scatter*, and shapeSize. New simplified AST (in package accelerate-backend-kit) for backend writers who want to avoid the complexities of the type-safe AST.

• 0.10.0.0: Complete sharing recovery for scalar expressions (but currently disabled by default). Also bug fixes in array sharing recovery and a few new convenience functions.

• 0.9.0.0: Streaming, precompilation, Repa-style indices, stencils, more scans, rank-polymorphic fold, generate, block I/O & many bug fixes.

• 0.8.1.0: Bug fixes and some performance tweaks.

• 0.8.0.0: replicate, slice and foldSeg supported in the CUDA backend; frontend and interpreter support for stencil. Bug fixes.

• 0.7.1.0: The CUDA backend and a number of scalar functions.

Hackage note

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

• Data.Array.Accelerate

• Data.Array.Accelerate.Interpreter

• Data.Array.Accelerate.Data.Complex