Cython is widely accessible

• Cython is bundled with Enthought Python Distribution, SAGE, and PythonXY
• Cython only requires Python and GCC, so it's installable basically anywhere
• Cython is easy_installable.
• mpi4py is already written in Cython.

In short: I'll bet dollars to donuts that anywhere FiPy is installable, Cython is too.

Building Cython code

1. Write up a module foo.pyx in Cython.
2. Write a setup.py which lists foo.pyx as an extension and specifies a cmdclass entry to compile it. Let's call that entry "build_ext"
3. Run

   python setup.py build_ext --inplace
   

   This compiles the foo module from Cython down to C.
4. Use foo as you would a Python module, despite the fact that it's (supposedly well-optimized) C code.

More details are available ​here.

Cython integrates with NumPy

See ​here. Further details are available in ​Seljebotn's paper.

An interesting excerpt from the paper:

For the algorithms which are expressible as NumPy operations, the speedup is much lower, ranging from no speedup to around ten times. The Cython code is usually much more verbose and requires more decisions to be made at compile-time. Use of Cython in these situations seems much less clear cut. A good approach is to prototype using pure Python, and, if it is deemed too slow, optimize the important parts after benchmarks or code profiling.

Most arithmetic algorithms in FiPy are specified in terms of NumPy operations, so Cython's use to us may be questionable. In a direct translation from a NumPy-operation-based routine to typed Cython code (using cdef np.ndarray), I saw no speedup.