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Why physicists still use Fortran

“I don’t know what the programming language of the year 2000 will look like, but I know it will be called FORTRAN.” –  Charles Anthony Richard Hoare, circa 1982

Fortran is rarely used today in industry — one ranking ranks it behind 29 other languages. However, Fortran is still a dominant language for the large scale simulation of physical systems, ie. for things like the astrophysical modeling of stars and galaxies, hydrodynamics codes (cf. Flash), large scale molecular dynamics, electronic structure calculation codes (cf. SIESTA), large scale climate models, etc.  In the field of high performance computing (HPC), of which large scale numerical simulation is a subset, there are only two languages in use today — C/C++ and “modern Fortran” (Fortran 90/95/03/08). The popular Open MPI libraries for parallelizing code were developed for these two languages. So basically, if you want fast code that an run on many processors, you are limited to these two options. Modern Fortran also has a feature called ‘coarrays‘ which puts parallelization features directly into the language. Coarrays started as an extension of Fortran 95 and were incorporated into Fortran 2008 standard.

The heavy use of Fortran by physicists often confounds computer scientists and other outsiders who tend to view Fortran as a historical anachronism.

What I would like to do in this article is explain why Fortran is still a useful language. I am not advocating that physics majors learn Fortran — since most physics majors will end up in research, their time may be better invested in learning C/C++ (or just sticking with Matlab/Octave/Python). What I would like to explain is why Fortran is still used, and show that it is not merely because physicists are ‘behind the time’ (although this is sometimes true – about a year ago I saw a physics student working on a Fortran 77 code, and both the student and adviser were unaware of Fortran 90). Computer scientists should (and do) consider the continued dominance of Fortran in numerical computing as a challenge. Continue reading

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