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Comment by sseagull | Hacker News Reader

Well, it's moving, not moved yet.

New libraries are being written in C/C++, and maybe Python. This includes libraries that should form the foundation of the QM community (matrix/tensor and integral libraries). These are meant to take advantage of newer hardware and libraries which themselves are written in C/C++, and often in a way that is inaccessible from Fortran.

The old Fortran code will be around for a long time, but I don't know of any large-scale, serious efforts to develop new packages or major new functionality that are starting with Fortran.

(I'm not totally against Fortran - I just spend a week devloping in it. But I still much prefer C++ and Python)

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I don't understand why the implementation language of low level libraries should determine a high level language that uses them. In what way are C libraries inaccessible from Fortran, given that it defines interoperability with C?

I'm afraid you need a 10-, or preferably 20-, year perspective, not a week.

I have been developing in Fortran for years. I just mentioned that as an aside.

About C compatiblity: Many C libraries use pointers in their interfaces. Interoperability is indeed defined by the Fortran 2003 standard, which I have used several times to wrap existing C libraries. However, much of the existing code is F90 only (some even F77...), and a vast majority of Fortran developers in the field are not familiar with even modules and other F90 features, let alone iso_c_binding in 2003.

Also, newer libraries tend to be C++ as well, which is more difficult (or at least more awkward) to wrap.

It’s partly the power of library authoring that is moving things I think. To my knowlegdge most post lapack tensor algebra is all C/C++ also. I don’t know Fortran but I am not sure if it has the same flexibility when it comes to generic code and things like writing things like template expression math codes.

Finally the fact that groups like Facebook and google are writing their machine learning code in C++ shows that 1 they find it useful and 2 plenty performant. This kinda became a response to the comment above yours sorry.

If you don't mind, I'd like to maybe chat with you a bit and get your opinion on some things (and maybe see if I've actually met you before). My (mainly throwaway) email is ytterbium35 (at major email service run by google).

If you don't feel like it, feel free to ignore.

Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) is written in C++ and it's over 20 years old. The FORTRAN codebases seem to be centered around the finite elements/difference methods and the fluid dynamics community.

Sorry, I wasn't specific. My comment mostly applies to the quantum mechanics (QM) community, rather than molecular dynamics. In QM, many people still run Gaussian/GAMESS/ADF/MolCAS/MolPro/Dalton, which are all Fortran (or majority Fortran). And most are Fortran 90 or earlier.

My background is in QM, so I guess that's my bias showing through :)

Even in Molecular dynamics, there's dl_poly, for instance. Other things that come to mind in Fortran apart from nwchem are cp2k and castep.

Somewhere under http://www.archer.ac.uk, there's a summary of the time used by various codes on that UK "Tier 1" system.

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