by Ian Ozsvald for EuroPython 2011
UPDATE - post-event I’ve created a 49 page PDF write-up which summarises the 4 hour tutorial
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):
Some background reading: