[jarvist]

My experience is a first implementation / novice programmer will write Julia code of a similar speed to python. But then an intermediate Julia programmer can adapt that same code to be of order C / Fortran performance, without stopping the novice programmer from being able to work on the code base. So it's this ability to iteratively improve and collaborate across very different skill sets that's really important.

This is quite a different situation to traditional scientific computing.

[reachtarunhere]

> My experience is a first implementation / novice programmer will write Julia code of a similar speed to python.

No because of JIT compilation he would write code faster than Python by default. Now to truly rival optimized C++ code one has to do the tricks mentioned in this post like optimizing memory access, SIMD and maximizing instruction parallelism.

The key point is you are better off by default and can do some ugly stuff in the critical parts of the code while still using the same language.

[eigenspace]

It really depends on what you end up doing. A lot of "python" code is really just thin wrappers around fairly optimized C routines (although there's inefficiencies from the wrapper, and the optimization barriers), so if you're doing something where the bulk of the work is happening inside those routines, beginner-written julia code will end up being roughly comparable to expert-written numpy code or whatever.

But yeah if you're writing a loop or something else where the majority of work is actually being done by python itself, then it's going to typically be much much slower than the equivalent julia code.