If you use Python for anything remotely performance sensitive, I highly recommend giving Cython a shot. The trick is to find your computation-intensive inner lo

If you use Python for anything remotely performance sensitive, I highly recommend giving Cython a shot. The trick is to find your computation-intensive inner loop stuff, move it off to its own module, add types, and go. It makes your build a little more complicated (i.e. most Python projects have no build process at all) but if you're in the niche where it's useful, it's damned useful. Here what it does. Take a look at this Python code: import math def distance(x1, y1, x2, y2): return math.sqrt((y2-y1)**2+(x2-x1)**2) It looks simple, but it's doing a lot behind the scenes. It respects __sub__/__add__ metamethods, respects and promotes between floats/longs/ints and even Bignums, it has to do a __getattr__ on the `math` object to find the sqrt function in the first place, etc (and can't cache that because this is Python so you may have changed it in between calls). After every one of these calls, a Python exception is checked for, and many reference counts are incremented and decremented. Every now and then, the interpreter checks to see if it's time to release/reacquire the GIL and if any OS signals have come in. And it does all of this by compiling Python to bytecode and running that through its interpreter. Here's the same code in Cython: from libc.math cimport sqrt cdef double distance(double x1, double y1, double x2, double y2): return sqrt((y2-y1)**2+(x2-x1)**2) Cython takes that code and compiles it to this C code (a bit simplified): #include <math.h> double distance(double x1, double y1, double x2, double y2) { double a = y2-y1; double b = x2-x1; return sqrt(a*a+b*b) } So now instead of running through dozens of intermediate internal Python functions, you're just loading the numbers into your FPU and doing the computation there. Of course, you've given up all of your Python dynamicness. Now this won't work with Bignums, it can't be monkey patched, and now you have a compiling step to running your Python code. You can do this to your existing code one function at a time, so you don't have to give those things up for your entire project, just where it matters. And where performance matters, it's a godsend I ported a small number of pieces of reddit to Cython. Here are some simple examples: * https://github.com/reddit/reddit/blob/master/r2/r2/lib/db/_sorts.pyx https://github.com/reddit/reddit/blob/master/r2/r2/lib/db/_s... * https://github.com/reddit/reddit/blob/master/r2/r2/lib/utils/_utils.pyx https://github.com/reddit/reddit/blob/master/r2/r2/lib/utils... * https://github.com/reddit/reddit/blob/master/r2/r2/lib/mr_tools/_mr_tools.pyx https://github.com/reddit/reddit/blob/master/r2/r2/lib/mr_to... * https://github.com/reddit/reddit/blob/master/r2/r2/lib/wrapped.pyx https://github.com/reddit/reddit/blob/master/r2/r2/lib/wrapp... As the underscore naming implies, these are only selected performance or compute sensitive functions factored out of their normal non-underscore Python modules which generally wrap them. These functions were coming up disproportionately in profiling because they're called very often and/or do a lot of arithmetic, and afterwards they are entirely nonissues. Often something like a _sorts.pyx:_confidence call got over 25 times faster. I've also talked about the downsides in a thread here[1]. Go in knowing that stuff too, but after my excellent experience with Cython I'm a True Believer. [1] https://www.reddit.com/r/Python/comments/2xlya2/anyone_willing_to_share_experiences_of_cython/cp1ct6a?context=3 https://www.reddit.com/r/Python/comments/2xlya2/anyone_willi...