Description

An embedded language for accelerated array processing.

Description

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.

See the Data.Array.Accelerate module for further information.

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-examples: Computational kernels and applications demonstrating the use of Accelerate.
accelerate-io*: Fast conversions between Accelerate arrays and other array and data formats.
accelerate-fft: Discrete Fourier transforms, with FFI bindings to optimised implementations.
accelerate-blas: Numeric linear algebra, with FFI bindings to optimised implementations.
accelerate-bignum: Fixed-width large integer arithmetic.
containers-accelerate: Container types for use with Accelerate.
hashable-accelerate: Class for types which can be converted to a hash value.
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

The accelerate-examples package demonstrates a range of computational kernels and several complete applications, including:

An implementation of the Canny edge detection algorithm
Interactive Mandelbrot and Julia set generators
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 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 mesh.

Mailing list and contacts

Gitter chat: https://gitter.im/AccelerateHS/Lobby
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

README.md

hackage.haskell.org

High-performance parallel arrays for Haskell

Data.Array.Accelerate defines an embedded language of array 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 are online-compiled and executed on a range of architectures.

For more details, see our papers:

There are also slides from some fairly recent presentations:

Chapter 6 of Simon Marlow's book Parallel and Concurrent Programming in Haskell contains a tutorial introduction to Accelerate.

Trevor's PhD thesis details the design and implementation of frontend optimisations and CUDA backend.

Table of Contents

An Embedded Language for Accelerated Array Computations

A simple example

As a simple example, consider the computation of a dot product of two vectors of single-precision 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.run it may be on-the-fly off-loaded to a GPU.

Availability

Package accelerate is available from

Hackage: accelerate - install with cabal install accelerate
GitHub: AccelerateHS/accelerate - get the source with git clone https://github.com/AccelerateHS/accelerate.git. The easiest way to compile the source distributions is via the Haskell stack tool.

Additional components

The following supported add-ons are available as separate packages:

accelerate-llvm-native: Backend targeting multicore CPUs
accelerate-llvm-ptx: Backend targeting CUDA-enabled NVIDIA GPUs. Requires a GPU with compute capability 2.0 or greater (see the table on Wikipedia)
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 conversion between Accelerate arrays and other array formats (for example, Repa and Vector)
accelerate-fft: Fast Fourier transform implementation, with FFI bindings to optimised implementations
accelerate-blas: BLAS and LAPACK operations, 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
numeric-prelude-accelerate: Lifting the numeric-prelude to Accelerate
wigner-ville-accelerate: Wigner-Ville time-frequency distribution.

Install them from Hackage with cabal install PACKAGENAME.

Documentation

Haddock documentation is included and linked with the individual package releases on Hackage.
Haddock documentation for in-development components can be found here.
The idea behind the HOAS (higher-order abstract syntax) to de-Bruijn conversion used in the library is described separately.

Examples

accelerate-examples

The accelerate-examples package provides a range of computational kernels and a few complete applications. To install these from Hackage, issue cabal install accelerate-examples. The examples include:

An implementation of canny edge detection
An interactive mandelbrot set generator
An N-body simulation of gravitational attraction between solid particles
An implementation of the PageRank algorithm
A simple 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

LULESH-accelerate is in implementation of the Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics (LULESH) mini-app. LULESH represents a typical hydrodynamics code such as ALE3D, but is a highly simplified application, hard-coded to solve the Sedov blast problem on an unstructured hexahedron mesh.

LULESH mesh

Additional examples

Accelerate users have also built some substantial applications of their own. Please feel free to add your own examples!

Jonathan Fraser, GPUVAC: An explicit advection magnetohydrodynamics simulation
David van Balen, Sudokus: A sudoku solver
Trevor L. McDonell, lol-accelerate: A backend to the Λ ○ λ (Lol) library for ring-based lattice cryptography
Henning Thielemann, patch-image: Combine a collage of overlapping images
apunktbau, bildpunkt: A ray-marching distance field renderer
klarh, hasdy: Molecular dynamics in Haskell using Accelerate
Alexandros Gremm used Accelerate as part of the 2014 CSCS summer school (code)

Who are we?

The Accelerate team (past and present) consists of:

Manuel M T Chakravarty (@mchakravarty)
Gabriele Keller (@gckeller)
Trevor L. McDonell (@tmcdonell)
Robert Clifton-Everest (@robeverest)
Frederik M. Madsen (@fmma)
Ryan R. Newton (@rrnewton)
Joshua Meredith (@JoshMeredith)
Ben Lever (@blever)
Sean Seefried (@sseefried)
Ivo Gabe de Wolff (@ivogabe)

The maintainer and principal developer of Accelerate is Trevor L. McDonell [email protected].

Mailing list and contacts

Mailing list: [email protected] (discussions on both use and development are welcome)
Sign up for the mailing list at the Accelerate Google Groups page
Bug reports and issues tracking: GitHub project page
Chat with us on gitter

Citing Accelerate

If you use Accelerate for academic research, you are encouraged (though not required) to cite the following papers:

Manuel M. T. Chakravarty, Gabriele Keller, Sean Lee, Trevor L. McDonell, and Vinod Grover. Accelerating Haskell Array Codes with Multicore GPUs. In DAMP '11: Declarative Aspects of Multicore Programming, ACM, 2011.
Trevor L. McDonell, Manuel M. T. Chakravarty, Gabriele Keller, and Ben Lippmeier. Optimising Purely Functional GPU Programs. In ICFP '13: The 18th ACM SIGPLAN International Conference on Functional Programming, ACM, 2013.
Robert Clifton-Everest, Trevor L. McDonell, Manuel M. T. Chakravarty, and Gabriele Keller. Embedding Foreign Code. In PADL '14: The 16th International Symposium on Practical Aspects of Declarative Languages, Springer-Verlag, LNCS, 2014.
Trevor L. McDonell, Manuel M. T. Chakravarty, Vinod Grover, and Ryan R. Newton. Type-safe Runtime Code Generation: Accelerate to LLVM. In Haskell '15: The 8th ACM SIGPLAN Symposium on Haskell, ACM, 2015.
Robert Clifton-Everest, Trevor L. McDonell, Manuel M. T. Chakravarty, and Gabriele Keller. Streaming Irregular Arrays. In Haskell '17: The 10th ACM SIGPLAN Symposium on Haskell, ACM, 2017.

Accelerate is primarily developed by academics, so citations matter a lot to us. As an added benefit, you increase Accelerate's exposure and potential user (and developer!) base, which is a benefit to all users of Accelerate. Thanks in advance!

What's missing?

Here is a list of features that are currently missing:

Preliminary API (parts of the API may still change in subsequent releases)
Many more features... contact us!

accelerate

High-performance parallel arrays for Haskell

A simple example

Availability

Additional components

Documentation