Julia motivation: why weren't Numpy, Scipy, Numba, good enough?

[stevengj]

@Pierre_Augier

Pythran compiles in optimized C++ any simple Python functions using numpy and scipy. A very nice thing is that you don’t need to decompose vectorized code in loops. Pythran is fully aware of numpy syntax.

As was commented by several people earlier in this thread, the performance advantage of Julia (unlike Cython, Numba, or Pythran) is that good performance is not limited to a single “built-in” container type (NumPy arrays) of a small set of built-in scalar types, and “built-in” vectorized functions recognized by the compiler. In Julia, you can get high performance in code that fully uses polymorphism, user-defined types, user-defined containers, and user-defined vectorized functions (or without using a vectorized style at all).