Well, in total honesty, the very fact of using PyPy is already a performance optimization over the regular CPython interpreter. (Although, this comparison is v

Well, in total honesty, the very fact of using PyPy is already a performance optimization over the regular CPython interpreter. (Although, this comparison is very interesting, it shows that not always PyPy will overperform regular CPython; it depends on what you are trying to do and if the system has just bootstrapped (after all, PyPy is a JIT compiler)): https://lincolnloop.com/blog/speed-comparison-cpython-pypy-pyston/ https://lincolnloop.com/blog/speed-comparison-cpython-pypy-p... All in all, i'm happy that there are alternatives to speed Python up, but i'm kind of a bit worried that the bottleneck on performance might be the Global Interpreter Lock (GIL), the big white-elephant-in-the-room that is never talked about often enough when discussing Python. Mind you, Python is one of my favorite languages and i've used it a lot, so no hate here. There are projects for overcoming the GIL, for example PyPy with Software Transactional Memory, but they're still work in progress: http://doc.pypy.org/en/latest/stm.html http://doc.pypy.org/en/latest/stm.html