PyOpenCL lets you access GPUs and other massively parallel compute
devices from Python. It tries to offer computing goodness in the
spirit of its sister project PyCUDA:
- Object cleanup tied to lifetime of objects. This idiom, often
in C++, makes it much easier to write correct, leak- and
- Completeness. PyOpenCL puts the full power of OpenCL’s API at
your disposal, if you wish. Every obscure get_info() query and
all CL calls are accessible.
- Automatic Error Checking. All CL errors are automatically
translated into Python exceptions.
- Speed. PyOpenCL’s base layer is written in C++, so all the niceties
above are virtually free.
- Helpful and complete Documentation
as well as a Wiki.
- Liberal license. PyOpenCL is open-source under the
and free for commercial, academic, and private use.
- Broad support. PyOpenCL was tested and works with Apple’s, AMD’s, and Nvidia’s
What you’ll need:
- gcc/g++ at or newer than version 4.8.2 and binutils at or newer than 22.214.171.124.1-10
(CentOS version number).
On Windows, use the mingwpy compilers.
- numpy, and
- an OpenCL implementation. (See this howto for how to get one.)
Places on the web related to PyOpenCL:
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.