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Not only "climate models"... a large chunk of scipy is just a thin Python layer over decades-old Fortran code. That many physicists chose to use the real thing instead of the fisher-price interface speaks in favor of them.
Not only "climate models"... a large chunk of scipy is just a thin Python layer over decades-old Fortran code. That many physicists chose to use the real thing instead of the fisher-price interface speaks in favor of them.
Gonum wraps the same code, from what i could tell fortran seem to have a neat way to handle more specialised number systems like dual numbers and hyperbolic numbers.
The premise of the article is that Fortran, 70 years later is still an appropriate tool to use for crunching numbers which it absolutely is but it neglects one major problem.
Like the COBOL issue that was all the rage 20 years ago, it is difficult to hire younger generation programmers that want to and are excited to develop in Fortran.
I mostly used C for my (small-scale) HPC work in grad school because it’s what I knew best, but at several points I wished I had learned Fortran instead.
Probably one of the only “higher level” languages that’s ever been used for serious petascale scientific computing is Julia (first with Celeste on the astro side, possibly soon with CliMA for climate modeling), which not coincidentally follows similar array indexing conventions as FORTRAN. And while that’s what I mostly use now, I don’t see Fortran going away any time soon.
If anything, with cool new projects like LFortran [1] making it possible to use Fortran more interactively, it’s probably quite a good time to learn modern Fortran!
Are modern supercomputers faster than a cluster of consumer-grade GPU cards?
1. There are thousands of scientific subroutines written in Fortran that are stable and well tested - in fact, there are well established formal libraries of them that go back over 60 years. The researchers know and trust them. 2. Despite the sneers and derision that Fortran has been subjected to from non-Fortran programmers over recent decades, Fortran is an excellent language to do intensive scientific and mathemat…
It took until C99 for C to have complex numbers and `restrict`. Those are two features FORTRAN has had for a much longer time, right?
Use it wrong, break the compiler assumptions and the bug hunting fun starts.
How is Fortran coming along with GPUs? (last I looked it was being done with proprietary compiler language extensions, but that was a while ago) Are modern supercomputers faster than a cluster of consumer-grade GPU cards?
On the top500 list, #1 does 400,000 TFlop/s, #500 does 1000 TFlop/s. How much would the kind of GPU cluster you're thinking of do?
What practical problems does Fortran cause when used for numerical computing?