- This first from 2016 by Victor Zverovich talks about startup times and memory footprint. The examples used are small, so may not be indicative of performance for larger more numerically-intensive code.
- This second from 2019 by Eduardo Alvarez takes a very close look at what is needed to get C++-like speed from Julia. The way to do that is not to take advantage of Python-like syntax, and instead code Julia like it was C++.
It seems like Julia would be good for a Python scientific programmer, who wants to increase performance greatly, but not switch to the somewhat more complicated programming involved in using C++.