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accelerate: An embedded language for accelerated array processing

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Warnings:

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>

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:

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
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Properties

Versions 0.4.0, 0.5.0.0, 0.6.0.0, 0.7.1.0, 0.8.0.0, 0.8.1.0, 0.9.0.0, 0.9.0.1, 0.10.0.0, 0.12.0.0, 0.12.1.0, 0.12.2.0, 0.13.0.0, 0.13.0.1, 0.13.0.2, 0.13.0.3, 0.13.0.4, 0.13.0.5, 0.14.0.0, 0.15.0.0, 0.15.1.0, 1.0.0.0, 1.1.0.0, 1.1.1.0, 1.2.0.0, 1.2.0.1, 1.3.0.0, 1.3.0.0
Change log CHANGELOG.md
Dependencies ansi-terminal (>=0.6.2), async (>=2.0), base (>=4.12 && <4.15), base-orphans (>=0.3), bytestring (>=0.10.2), containers (>=0.3), cryptonite (>=0.21), deepseq (>=1.3), directory (>=1.0), ekg (>=0.1), ekg-core (>=0.1), exceptions (>=0.6), filepath (>=1.0), ghc-prim, half (>=0.3), hashable (>=1.1), hashtables (>=1.2.3), hedgehog (>=0.5), lens (>=4.0), mtl (>=2.0), prettyprinter (>=1.2), prettyprinter-ansi-terminal (>=1.0), primitive (>=0.6.4), tasty (>=0.11), tasty-expected-failure (>=0.11), tasty-hedgehog (>=0.1), tasty-hunit (>=0.9), template-haskell, terminal-size (>=0.3), text (>=1.2), transformers (>=0.3), unique, unix, unordered-containers (>=0.2), vector (>=0.10), Win32 [details]
License BSD-3-Clause
Author The Accelerate Team
Maintainer Trevor L. McDonell <trevor.mcdonell@gmail.com>
Category Accelerate, Compilers/Interpreters, Concurrency, Data, Parallelism
Home page https://github.com/AccelerateHS/accelerate/
Bug tracker https://github.com/AccelerateHS/accelerate/issues
Source repo head: git clone git://github.com/AccelerateHS/accelerate.git
this: git clone git://github.com/AccelerateHS/accelerate.git(tag v1.3.0.0)
Uploaded by TrevorMcDonell at 2020-08-28T11:10:21Z

Modules

[Index] [Quick Jump]

Flags

Automatic Flags
NameDescriptionDefault
debug

Enable debug tracing messages. The following options are read from the environment variable ACCELERATE_FLAGS, and via the command-line as:

./program +ACC ... -ACC

Note that a backend may not implement (or be applicable to) all options.

The following flags control phases of the compiler. The are enabled with -f<flag> and can be reveresed with -fno-<flag>:

  • acc-sharing: Enable sharing recovery of array expressions (True).

  • exp-sharing: Enable sharing recovery of scalar expressions (True).

  • fusion: Enable array fusion (True).

  • simplify: Enable program simplification phase (True).

  • inplace: Enable in-place array updates (True).

  • flush-cache: Clear any persistent caches on program startup (False).

  • force-recomp: Force recompilation of array programs (False).

  • fast-math: Allow algebraically equivalent transformations which may change floating point results (e.g., reassociate) (True).

  • fast-permute-const: Allow non-atomic `permute const` for product types (True).

The following options control debug message output, and are enabled with -d<flag>.

  • verbose: Be extra chatty.

  • dump-phases: Print timing information about each phase of the compiler. Enable GC stats (+RTS -t or otherwise) for memory usage information.

  • dump-sharing: Print information related to sharing recovery.

  • dump-simpl-stats: Print statistics related to fusion & simplification.

  • dump-simpl-iterations: Print a summary after each simplifier iteration.

  • dump-vectorisation: Print information related to the vectoriser.

  • dump-dot: Generate a representation of the program graph in Graphviz DOT format.

  • dump-simpl-dot: Generate a more compact representation of the program graph in Graphviz DOT format. In particular, scalar expressions are elided.

  • dump-gc: Print information related to the Accelerate garbage collector.

  • dump-gc-stats: Print aggregate garbage collection information at the end of program execution.

  • dubug-cc: Include debug symbols in the generated and compiled kernels.

  • dump-cc: Print information related to kernel code generation/compilation. Print the generated code if verbose.

  • dump-ld: Print information related to runtime linking.

  • dump-asm: Print information related to kernel assembly. Print the assembled code if verbose.

  • dump-exec: Print information related to program execution.

  • dump-sched: Print information related to execution scheduling.

Disabled
ekg

Enable hooks for monitoring the running application using EKG. Implies debug mode. In order to view the metrics, your application will need to call Data.Array.Accelerate.Debug.beginMonitoring before running any Accelerate computations. This will launch the server on the local machine at port 8000.

Alternatively, if you wish to configure the EKG monitoring server you can initialise it like so:

import Data.Array.Accelerate.Debug

import System.Metrics
import System.Remote.Monitoring

main :: IO ()
main = do
  store  <- initAccMetrics
  registerGcMetrics store      -- optional

  server <- forkServerWith store "localhost" 8000

  ...

Note that, as with any program utilising EKG, in order to collect Haskell GC statistics, you must either run the program with:

+RTS -T -RTS

or compile it with:

-with-rtsopts=-T
Disabled
bounds-checks

Enable bounds checking

Enabled
unsafe-checks

Enable bounds checking in unsafe operations

Disabled
internal-checks

Enable internal consistency checks

Disabled
nofib

Build the nofib test suite (required for backend testing)

Disabled

Use -f <flag> to enable a flag, or -f -<flag> to disable that flag. More info

Downloads

Maintainer's Corner

Package maintainers

For package maintainers and hackage trustees

Readme for accelerate-1.3.0.0

[back to package description]
henlo, my name is Theia

High-performance parallel arrays for Haskell

GitHub CI Gitter
Stackage LTS Stackage Nightly Hackage

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

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

Additional components

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

Install them from Hackage with cabal install PACKAGENAME.

Documentation

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:

Mandelbrot Raytracer

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!

Who are we?

The Accelerate team (past and present) consists of:

The maintainer and principal developer of Accelerate is Trevor L. McDonell trevor.mcdonell@gmail.com.

Mailing list and contacts

Citing Accelerate

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

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: