accelerate
An embedded language for accelerated array processing https://github.com/AccelerateHS/accelerate/
Version on this page: | 0.15.1.0 |
LTS Haskell 11.6: | 1.1.1.0 |
Stackage Nightly 2018-03-12: | 1.1.1.0 |
Latest on Hackage: | 1.2.0.0 |
Module documentation for 0.15.1.0
- Data
- Data.Array
- Data.Array.Accelerate
- Data.Array.Accelerate.Data
- Data.Array.Accelerate.Interpreter
- Data.Array.Accelerate
- Data.Array
An Embedded Language for Accelerated Array Computations
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:
- Accelerating Haskell Array Codes with Multicore GPUs
- Optimising Purely Functional GPU Programs (slides)
- Embedding Foreign Code
- Type-safe Runtime Code Generation: Accelerate to LLVM (slides) (video)
There are also slides from some fairly recent presentations:
- Embedded Languages for High-Performance Computing in Haskell
- GPGPU Programming in Haskell with Accelerate (video) (workshop)
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 - Availability - Additional components - Requirements - Documentation - Examples - Mailing list and contacts - Citing Accelerate - What's missing?
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.
Λ ○ λ (Lol)
Λ ○ λ (Lol) is a general-purpose library for ring-based lattice cryptography. Lol has applications in, for example, symmetric-key somewhat-homomorphic encryption schemes. The lol-accelerate package provides an Accelerate backend for Lol.
Additional examples
Accelerate users have also built some substantial applications of their own. Please feel free to add your own examples!
- 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)
Mailing list and contacts
- Mailing list:
accelerate-haskell@googlegroups.com
(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.
The maintainers of Accelerate are Manuel M T Chakravarty and Trevor L McDonell .
Citing Accelerate
If you use Accelerate for academic research, you are encouraged (though not required) to cite the following papers (BibTeX):
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.
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)
Changes
Change Log
Notable changes to the project will be documented in this file.
The format is based on Keep a Changelog and the project adheres to the Haskell Package Versioning Policy (PVP)
1.2.0.0 - 2018-04-03
Changed
- Internal debugging/RTS options handling has been changed. Compiling this package now implies that backends are also compiled in debug mode (no need to set the
-fdebug
cabal flag for those packages as well). - Complex numbers are stored in the C-style array-of-struct representation.
- Improve numeric handling of complex numbers.
- Coercions (
bitcast
) now occur between the underlying representation types - Front-end performance improvements
Added
- Support for half-precision floating-point numbers.
- Support for struct-of-array-of-struct representations. Currently this is limited to fields of 2,3,4,8, or 16-elements wide.
- Add equivalents for
Data.Functor
,Data.Semigroup
(ghc-8+) - Add instances and helper functions for
Maybe
andEither
types - Add rank generalised versions of
take
,drop
,head
,tail
,init
,slit
,reverse
andtranspose
. - Implement counters and reporting for
-ddump-gc-stats
Contributors
Special thanks to those who contributed patches as part of this release:
- Trevor L. McDonell (@tmcdonell)
- Ryan Scott (@ryanglscott)
- Rinat Striungis (@Haskell-mouse)
1.1.1.0 - 2017-09-26
Changed
Improve and colourise the pretty-printer
1.1.0.0 - 2017-09-21
Added
Additional EKG monitoring hooks (#340)
Operations from
RealFloat
Changed
- Changed type of
scanl'
,scanr'
to return anAcc
tuple, rather than a tuple ofAcc
arrays. - Specialised folds
sum
,product
,minimum
,maximum
,and
,or
,any
,all
now reduce along the innermost dimension only, rather than reducing all elements. You can recover the old behaviour by firstflatten
-ing the input array. - Add new stencil boundary condition
function
, to apply the given function to out-of-bounds indices.
Fixed
1.0.0.0 - 2017-03-31
- Many API and internal changes
- Bug fixes and other enhancements
0.15.1.0
Fix type of
allocateArray
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 in the CUDA backend.
- 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
(both including a graphical frontend):- A real-time Canny edge detection
- An interactive fluid flow simulator
- Bug fixes.
0.11.0.0
- New Prelude-like functions
zip*
,unzip*
,fill
,enumFrom*
,tail
,init
,drop
,take
,slit
,gather*
,scatter*
, andshapeSize
. - 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 computations
- Precompilation
- Repa-style array indices
- Additional collective operations supported by the CUDA backend:
stencil
s, morescan
s, rank-polymorphicfold
,generate
. - Conversions to other array formats
- Bug fixes
0.8.1.0
Bug fixes and some performance tweaks.
0.8.0.0
- More collective operations supported by the CUDA backend:
replicate
,slice
andfoldSeg
. Frontend and interpreter support forstencil
. - Bug fixes.
0.7.1.0
Initial release of the CUDA backend