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I've started learning Julia lately, and it also has an appeal, especially for fortran-ish computations. (Yup, I said fortran ... it is still used in lots of sc
by bluenose69 6y ago
I've started learning Julia lately, and it also has an appeal, especially for fortran-ish computations. (Yup, I said fortran ... it is still used in lots of scientific computations e.g. fluid-mechanical models.)
- ur-whale 6y ago"used" as in "using existing libraries" or do you mean there are folks still writing fortran in 2020?
- mattkrause 6y agoI have a collaborator who was writing Fortran this summer (and not even in physics!). Oddly, it didn't seem like he was particularly locked in by a vast pile of legacy code; he just found it a good match for his mental model of the project.
- bluenose69 6y agoYes, people still write fortran in 2020. It's not just for legacy products (as, I think, Cobol is). Fortran is a very expressive and efficient language for high-performance computation. When you start a calculation that may take weeks to complete, you want to use a tool that you can trust. Fortran is a great language for legacy products, with code written 50 years ago still working fine. But it has also gained new features over the years, keeping it ahead of competitors in terms of its ability to harvest all a machine can deliver. This is all for technical work like numerical modelling. Scientists are a very practical lot, switching from Fortran to C (usually C++) when that makes sense, then to python, R, etc when that makes sense, and so forth. The original article was about scientists switching to rust. I don't know anyone who has done that. I do know many who are exploring Julia, though. Bear in mind that scientists tend to be quite good at technical work. Learning a new language is not a significant problem for them. But scientific programming is about much, much more than programming. The essence of scientific programming is in the underlying mathematics and science, and Fortran just happens to be pretty good at those things.