This is the second post on how to accelerate Python with Cython. The previous post used Cython to declare static C data types in Python, to speed up the runtime of a prime number generator. In this post we shall be modifying a more complex program, one that performs an image transform on a map. This will allow me to demonstrate some more advanced Cython techniques, such as importing C functions from the C math library, using memory views of Numpy arrays and turning off the Python global interpreter lock (GIL).
As with the previous post, we shall be making a series of Cython modifications to Python code, noting the speed improvement with each step. As we go, we’ll be using the Cython compiler’s annotation feature to see which lines are converted into C at each stage, and which lines are still using Python objects and functions. And as we tune the code to run at increasingly higher speeds, we shall be profiling it to see what’s still holding us up, and where to refocus our attention.
Although I will be using Python 3 on a Mac, the instructions I give will mostly be platform agnostic: I will assume you have installed Cython on your system (on Windows, Linux or OS/X) and have followed and understood the installation and testing steps in my previous post. This will be essential if you are to follow the steps I outline below. As stated in the previous post, Cython is not for Python beginners.
Alas, dear Windows, it was not to be. I’m afraid I’ve been seeing other platforms. Specifically, I’ve been spending time with OS/X behind your back. It was just too painful to be with you. All those arguments, the shouting, the hair-pulling, the throwing things across the room.
