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I research and teach programming languages, compilers, and their applications at the University of New South Wales (UNSW), Sydney. My main interest is in functional and parallel programming, and in particular, in Haskell.


My Twitter handle is TacticalGrace.
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accelerate

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GPU-accelerated array computations in Haskell

I just published stable release 0.12 of the embedded array language Accelerate for high-performance GPU computing on Hackage. The release includes example applications, such as a real-time Canny edge detector, a fluid flow simulator, and a quasicrystal animation, as well as example algorithms, such as radixsort, matrix computations, and a Black-Scholes option pricing model.

In the new modularised architecture, the Accelerate release comprises four packages:

  • accelerate (the main package providing the Accelerate language),
  • accelerate-io (conversion operations with other Haskell array libraries, including the data-parallel companion library Repa),
  • accelerate-cuda (backend targetting NVIDIA GPUs via the CUDA SDK), and
  • accelerate-examples (example applications and regression tests).

For more information, see Accelerate on GitHub. To use the CUDA backend, you need a CUDA-enabled NVIDIA GPU as well as having the CUDA SDK already installed.

Filed under  //  accelerate   gpgpu   haskell   parallelism  
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GPU computing in Haskell: version 0.10 of Data.Array.Accelerate

Accelerate is an embedded language for GPU-accelerated array computations in Haskell that targets NVIDIA's CUDA framework and also has an experimental OpenCL backend (that currently does not support the whole feature set of the language). I just released version 0.10.0.0 of Accelerate. A considerable amount of example code is in the companion package accelerate-examples. The main user-visible changes in this release are frontend bug fixes.

For more information, have a look at Accelerate on GitHub.

Filed under  //  accelerate   gpgpu   haskell  
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Released Data.Array.Accelerate 0.9.0.0 — the Haskell array library for GPUs

I just released accelerate 0.9.0.0 on Hackage. This is the version that has been available from the GitHub repository for a while (supporting shape polymorphism, stencil computations, block I/O, and much more), but adapted such that it works with the forthcoming GHC 7.4.1 release. (I tested it with 7.4.1 RC2).

It doesn't yet include Trevor's recent work that improved the CUDA backend in many significant ways — you can get that code from Trevor's fork on GitHub.

For more details, see the main GitHub repository and the GitHub wiki pages.

Filed under  //  accelerate   edsl   gpgpu   parallelism  
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Video and slides of "Data Parallelism in Haskell" @ Brisbane FP Group

The Brisbane FP Group (BFPG) kindly invited me to give a talk about our work on data parallel programming in Haskell. The talk motivates the use of functional programming for parallel, and in particular, data parallel programming and explains the difference between regular and irregular (or nested) data parallelism. It also discusses the Repa library (regular data parallelism for multicore CPUs), the embedded language Accelerate (regular data parallelism for GPUs), and Data Parallel Haskell (nested data parallelism for multicore CPUs).  The slides of the presentation are available in two formats: HTML5 slideshow and PDF. Thank you to everybody who attended. Special thanks go to OJ Reeves and Tony Morris for organising everything, to Rob Manthey for producing the video, to Mincom for the venue, and to Functional IO for the sponsorship.

Filed under  //  accelerate   dph   haskell   parallelism   repa  
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NVIDIA’s next generation GPU architecture has a lot for HPC to love

Yesterday, NVIDIA presented its next-generation GPU architecture, called "Fermi": http://insidehpc.com/2009/09/30/nvidia-next-generation-gpu-fermi-targets-hpc-...

In addition to more raw computing power, Fermi brings advances that make it even more interesting for general-purpose computations than the current NVIDIA GPUs. In particular, it seems to support function pointers —at least they claim support for virtual functions— and improved support for double-precision floating-point numbers as well as coarse-grained MIMD support in the form of "concurrent kernel execution".

Filed under  //  accelerate   fermi   gpgpu  
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