Programming languages compared, and why I’m sticking with Python until Julia grows up

Comparisons of programming languages abound, especially with regard to running speed. This paper in the Journal of Economic Dynamics and Control by Boragan Aruoba of the University of Maryland and Jesús Fernández-Villaverde of the University of Pennsylvania is yet another one, albeit in a peer-reviewed journal and with a procedure (value function iteration) that is common in my field. Main observations:

  • When it comes to speed nothing beats C++ and FORTRAN.
  • Julia performs really well: only 2.37 times the running time of C++.
  • Python and R are slowpokes, at about 45 (Python with the Pypy interpreter) to 491 times (R without its Compiler package) times the running time of C++.
  • Matlab is somewhat inbetween the slowpokes and the frontrunners, with about 9 times the running time of C++.
  • R, Matlab and Python can get a boost from just-in-time-compilers and C compilers (like Rcpp for R; or Numba or Cython for Python) that make their running time comparable to that of Julia (although Rcpp is still a bit disappointing: 5.4 times the C++ running time).
Adding my own considerations:
  • I tried C++ and gave up very quickly. I bloody hate it with a passion.
  • I find R not much better: even though it is a scripted language it is clumsy, illogical, and makes for horrible code. Rcpp worked fine until I found out that it cannot handle matrices with more than 2 dimensions. Speeding up your R code with bigger matrices requires the use of full-blown C++. In other words, two languages that I bloody hate.
  • I value my independence, so I prefer to work on my own laptop with my own licenses. That makes Matlab, with its steep license fee, a no-go.
  • I’ve experimented with Cython and it seems to work quite well. I love the accessibility and clear layout of Python; moreover, my university teaches Python twice a year in an undergrad course. The only real problem with Python is that it is reputedly difficult to parallellize.
  • One day I’ll start using Julia. It’s fast, accessible, and (so they say) easy to parallellize. But not until there is a stable version and a decent IDE.

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