<http://nvidia.com/cuda/>`_ parallel computation API from Python.
Several wrappers of the CUDA API already exist-so what's so special
about PyCUDA?
* Object cleanup tied to lifetime of objects. This idiom, often
called
`RAII <http://en.wikipedia.org/wiki/Resource_Acquisition_Is_Initialization>`_
in C++, makes it much easier to write correct, leak- and
crash-free code. PyCUDA knows about dependencies, too, so (for
example) it won't detach from a context before all memory
allocated in it is also freed.
* Convenience. Abstractions like pycuda.driver.SourceModule and
pycuda.gpuarray.GPUArray make CUDA programming even more
convenient than with Nvidia's C-based runtime.
* Completeness. PyCUDA puts the full power of CUDA's driver API at
your disposal, if you wish. It also includes code for
interoperability with OpenGL.
* Automatic Error Checking. All CUDA errors are automatically
translated into Python exceptions.
* Speed. PyCUDA's base layer is written in C++, so all the niceties
above are virtually free.
* Helpful `Documentation <http://documen.tician.de/pycuda>`_ and a
`Wiki <http://wiki.tiker.net/PyCuda>`_.
Relatedly, like-minded computing goodness for `OpenCL <http://khronos.org>`_
is provided by PyCUDA's sister project `PyOpenCL <http://pypi.python.org/pypi/pyopencl>`_.
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Release files for pycuda 2011.2
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