3 ms·

I don't know about other languages, but the authors did reach out to us to sort out performance issues with the Julia code.

• bradfordarner 12y ago
  That is very good to know. That reassures me a little. I was really impressed with the speed difference they noted between Python and R and the fact that Numba wasn't too much slower than C++ implementations. However, given the state of the R code I became wary of accepting the benchmarks at face value. I haven't made the jump from R to Python for statistical applications yet. So, this article gave me something to think about. In terms of programmer happiness, I'm willing to live with a little slower performance in Python and not have to spend hours in C++.
  • Fede_V 12y ago
    I've used Numba to speed up some tight numerical computations written on NumPy arrays and it's incredibly good - about as fast as rewriting it from scratch in Cython, but much, much faster. The flipside is that it only really results in a speed up for pure numerical code - if you have to use any Python objects, then you lose everything.