Python JITs are coming
>  If it needs to be fast for CPython, it has to be written in C, but if it needs to be fast for a JIT, you cannot use C. He showed a simple mysum() function that totaled up the elements in an iterable. If it is passed a Python object like list(range(N)), the JIT knows what it is and can do lots of optimizations. But if it is passed a NumPy array, which is "opaque C stuff", the JIT doesn't understand it, so it will have trouble even achieving the performance of a non-NumPy version on a JIT-less Python.There's a third way here - run the C code using the same JIT as the Python code, instead of compiling it natively.That might sound mind-bending, but C is just a language like any other. It's actually fairly simple and consistent to implement compared to something like Python. You can interpret and JIT compile it if you want to - there's no major magic to that.Then you can optimise the C code and the Python code at the same time, inline the two, do the same optimisations as you do on Python code, etc. We're using this technique to run Ruby C extensions in JRuby and the results so far are great - running real C extensions faster than native code because we can optimise both the Ruby and the C at the same time.http://chrisseaton.com/rubytruffle/cext/