I have some Iranian colleagues, and they have told me that at Iranian (engineering) universities MATLAB, Fortran, and Maple are still very common. The Google trends show that Python might be prevailing vs MATLAB (no idea whats with the spike in October 2019?):
I don’t want to downplay the conclusions of the paper or the impact of this journal, but I’ve been told from colleagues, that many Iranian universities require master students publish a paper as part of their degree requirements. If you look at this journal it was established in 2015, and the editorial board still constitutes mainly by Iranian scientists.
Recently, I came across this preprint
Lindner, M., Lincoln, L., Drauschke, F., Koulen, J. M., Würfel, H., Plietzsch, A., & Hellmann, F. (2020). NetworkDynamics. jl–Composing and simulating complex networks in Julia. arXiv preprint arXiv:2012.12696 . [2012.12696] NetworkDynamics.jl -- Composing and simulating complex networks in Julia
where the authors show Julia is faster for network modelling (ODE’s) than Fortran and Scipy. The ODE solver they used is an F77 code from Hairer (Fortran Codes). However the last part of their report states:
This argument also holds for the Fortran version, but only in the case of a very large experimental set - in the benchmark performed in this paper with 300 different combinations of system and initial conditions, the overall time to execute using Fortran is considerably faster than Julia. In the case of our group,the sparse-multiplication Fortran version took more developer time than the Julia version; a trend which we suspect holds given a user equally skilled in both languages. More topically: the Fortran version does not include the abstractions available in our software NetworkDyanmics.jl - a new ODE system described by a new set of equations would require only small modifications to the NetworkDyanmics.jl benchmarking script,but an entire rewrite of the Fortran program. Finally we note that it is possible to fully compile Julia programs to an executable which suffers no JIT cost, largely negating the discussion of startup time and JIT time.
What we can conclude is that if Fortran had good support of sparse matrices, and a good family of ODE solvers, and possibly some tools for automatic generation of Jacobians (SymPy?) it could still remain very competitive. However, given the amount of effort which has gone into the Julia differential equations packages, it might be interesting to just try and call Julia from Fortran (GitHub - brocolis/dynload-julia: Dynamically load Julia from Fortran) rather than duplicate all their work.