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PyOpenCL lets you access the OpenCL parallel computation API from Python. Here's what sets PyOpenCL apart:
See the PyOpenCL Documentation.
Or get it directly from my source code repository by typing
git clone --recursive http://git.tiker.net/trees/pyopencl.git
You may also browse the source.
Prerequisites: All you need is an OpenCL implementation. And Python obviously.
This October I had the honor of presenting my work on using Python with GPUs at PyData NYC 2012. Here's a video of my talk:
There was also a panel discussion on Python+Parallel that I was a part of--here's the video of that:
Also be sure to check out all the videos of the other great talks to see what you've missed.
If this sounds interesting to you, also be sure to check out their next conference, PyData Silicon Valley 2013. And please (continue to!) support NumFocus and check out what Continuum are doing for big data in Python. They deserve a lot of credit for bringing the Python community together at events like PyData.
Please click the following link to view the slides: pycuda-pyopencl-gtc-2010.pdf.
Update: Nvidia has posted a recording of the session. There's also a full list of sessions, with many talks that are worth being watched. In particular, I'd like to recommend the ones by Bryan Catanzaro on Copperhead, which is built on top of PyCUDA, by Tim Warburton on all things GPU-based discontinuous Galerkin. Also check out the poster on Atomic Hedgehog by Cyrus Omar.
Please click the following link to view the slides: pycuda-nvidia.pdf.
Update 2: Giancarlo Colasante has transcoded the above video into just 16 MB. You may download the resulting video here.