Metadata revisions for accelerate-0.15.0.0

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No. Time User SHA256
-r2 2014-09-15T17:38:56Z TrevorMcDonell 98cea47c7fdb595a54cb06751fe54eb800059e5a2b1f9699a65d4e845b55cd4c
  • Changed source-repository from

    source-repository head
        type:     git
        location: git://github.com/AccelerateHS/accelerate.git
    
    to
    source-repository this
        type:     git
        location: git://github.com/AccelerateHS/accelerate.git
        branch:   release/0.15
        tag:      0.15.0.0
    

-r1 2014-09-15T02:32:31Z TrevorMcDonell a7c40f6b7d6f6cb196310feb71b7f76ff4ba394cdcc67f4480e28a49f467c6c6
  • Changed description from

    @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: <accelerate-haskell@googlegroups.com> (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,
    @stencil@s, more @scan@s, 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.
    
    to
    @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: <accelerate-haskell@googlegroups.com> (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,
    @stencil@s, more @scan@s, 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"
    

-r0 2014-09-15T01:03:08Z TrevorMcDonell 55d117eb72d90cd6fa16988233a3675ffc4f2c2499f6e49b726a1fffad2576ad