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 presentations on Accelerate:
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
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:
To install the Haskell toolchain try GHCup.
Additional components
The following supported add-ons are available as separate packages:
These are all available on Hackage.
Documentation
- Haddock documentation is included and linked with the individual package releases on Hackage.
- 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. 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
To run these, either get the source from Hackage using cabal get accelerate-examples or clone the git repository, then use cabal run on the individual executables.
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.
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:
The maintainer and principal developer of Accelerate is Trevor L.
McDonell [email protected].
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!
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