Sessions at EuroPython 2011 about Performance on Wednesday 22nd June

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  • Exploit your GPU power with PyCUDA (and friends)

    by Stefano Brilli

    CUDA technology permits to exploit the power of modern NVIDIA GPUs. In this talk, after a brief introduction to GPU architecture, we will focus on how CUDA got inside Python through libraries like PyCUDA and others…

    By some examples we will show the main concepts and techniques for good GPU programming.

    This talk targets anyone who wants to know how to exploit this technology from Python, the suitable use cases, the using techniques and the do-not-using techniques to get the best from his own GPU

    At 12:15pm to 1:15pm, Wednesday 22nd June

    Coverage video

  • Experiences making CPU-bound tasks run much faster

    by Ian Ozsvald

    As a long-time R&D consultant I'm often working to make slow, experimental code run faster for tasks like physics simulation, flood modeling and natural language processing. Python allows a smooth progression from rough-and-ready (but slow) algorithms through to finely tuned tasks that efficiently use as much CPU power as you can bring to bear. Speed-ups of 10-500* can be expected for the Mandelbrot code we'll use.

    In this talk I'll cover a set of libraries that make CPU-bound tasks run much faster. We'll begin with a look at profiling using RunSnakeRun and line_profiler to identify our bottleneck. We'll take a look at slow algorithms in Python and how they can run faster using numpy and numexpr.

    Next we'll cover the use of multiprocessing to utilise multiple CPU cores along with Cython or ShedSkin to easily use C code in a friendly Python wrapper. Multiprocessing on a quad-core system can often provide a 4* speed-up for the right tasks. Next parallelpython will let us run our code on a network of machines.

    Finally we'll look at pyCUDA to utilise an NVIDIA GPU. CUDA can give the best improvements for mathematical problems (over 100* on the right tasks) but works on a narrower set of problems.

    How it'll work:
    The tutorial will be hands on, you'll be converting example files from normal Python to faster variants using the tools below. All of it is optional, you'll get the most benefit by having everything installed. We'll work in groups and open discussion is encouraged.

    NOTE - you are expected to have all these tools installed *before* the tutorial (if you don't, you might find it hard to follow what's going on!).

    I'll be using Python 2.7.1 on a Macbook (Snow Leopard). All of these tools run on Windows and Linux, as long as your versions are fairly recent everything should run just fine.

    My versions (roughly ordered by importance):
    Python 2.7.1
    RunSnakeRun 2.0.1b6 (with wxPython Unicode)
    line_profiler (1.0b2)
    Cython 0.14.1
    ShedSkin 0.7.1
    numpy 1.5.1
    numexpr 1.4.2
    ParallelPython 1.6.1
    pyCUDA HEAD from git as of 14th June 2011 (with CUDA 4.0 drivers)
    PyPy 1.5

    Some background reading:

    At 2:30pm to 6:30pm, Wednesday 22nd June