HPC folks who care about performance don't use gfortran.
It's borderline dishonest to compare rust performance unless the target compiler is something like Intel's Fortran compiler from the OneAPI suite, or the newer flang compiler.
I got paid to do the LAPACK port, back in the mid 2000s, for a federal contractor working on satellite imaging type apps. I was still a good coder, back then... Took me about a month, as I recall. Maybe 6 weeks.
But I'm one of those old-school HPC guys who believes that libraries are mostly irrelevant, and absolutely no substitute for compilers and targeted code generation.
Julia is cool, btw. It could very well end up supplanting Fortran, once they fix the poor performance code generation issues.
Back in the late 90s, there was a project at SGI, called Bali, to make all their pipelines work in IEEE 32-bit floating point (they were using Intel i860 chips) so that they could do HW rendering of scenes written in Pixar's Renderman language.
Sony copied that idea for the 1st Playstation, and then folks like NVidia & 3DLabs quickly followed suit, the idea being they would enable that functionality for games like Final Fantasy.
In the early 2000s, the HPC folks realized that you could use a GPU for physics & engineering codes, and here were are 20 yrs later.
I have 40+ yrs of HPC/AI apps/performance engineering experience & I was one of the 1st people to port LAPACK and a number of other numerical libs to CUDA. Moreover, many of those major DoE + AI sites are my customers.
You should not confuse AMD's general & long-standing indifference/incompetence wrt SW with the actual difficulty of providing a portable SW path for acceleration. As Woody Allen once said: "90% of success is showing up"
But what happened in AI, when, in a very short period of time, almost everyone moved away from writing their directly in CUDA, to writing them in frameworks like Tensorflow & PyTorch is all the evidence anyone need to show just how unsound that SW obstacle is.
Mostly true, and you'll get no argument from me on the AMD & Intel are fuckwits front. Intel does ok, but AMD in particular has completely dropped the ball on the SW front, and has been doing so for at least 25 yrs.
The point I was glibly trying to get across was that even a small effort on the part of AMD to treat the SW side as seriously as NVidia does would have yielded great benefits, and not have left them so far behind.
Also, there is a lot of work going on in the gcc & llvm toolchain to not only use OpenMP to target accelerators in computationally intensive loops but, in the case of llvm, to also target tensor instructions for more efficient code generation (https://lists.llvm.org/pipermail/llvm-dev/2021-November/1537...).
It took the AI folk less than 18 months to almost completely move away from CUDA to Tensorflow and then PyTorch... LLVM, imho, is going to do the same for Sci/Eng and general code bases in the next 2 years.
wish someone would redo these books, the way they did w. the Feynman lectures.
Some of the volumes were so badly printed, as to render them almost illegible.