[ynik]

> if your program is too slow, you can just $X

Also, some of those $X are mutually exclusive.

If you write a lot of the performance-sensitive code in C; you're going to have complex C data structures. Now if you want parallelism, well too bad: you can't use multiprocessing, because you can't easily share your C data structure across multiple processes.

To actually use multiple cores without getting killed by the GIL, you end up having to replace a lot of Python code with C -- not just the most performance critical portion.

Copying a multi-GB data structure for each CPU core would take way too much memory, so we tried doing stuff with shared memory, but it's complicated. We spent months of developer time on this and still can't really scale beyond two cores, for something that would be embarrassingly parallel in any other programming language :(

The "mixing C and Python" solution is a trap. If performance might be important at any point in the future and you use Python; better plan for a complete rewrite in a different language.

[d0mine]

I had success using Cython for performance (numerical simulations mostly). nogil is your friend for multthreading.