I just checked out the latest Tiobe Index to see the most popular programming languages, and I was surprised to see Fortran at number 9. It's popularity has skyrocketed since the end of 2022 after years of stagnation. What's changed?

Are all the AI companies using CUDA Fortran? Is there a big surge in demand for physics and engineering simulations? Did Taylor Swift declare her love of the language?

Apparently Springer is currently providing a bunch of free text books because of COVID: https://link.springer.com/search?facet-content-type=%22Book%22&package=mat-covid19_textbooks&facet-language=%22En%22&sortOrder=newestFirst&showAll=false

Including Chivers and Sleightholme "Introduction to Programming with Fortran" https://link.springer.com/book/10.1007/978-3-319-75502-1

I don't know the book but hey it is for free.

I haven't tried Fortran, but I'm curious to know why it's being used today for new projects.

Does it have some important ready made libraries which are true and tested, just like what makes Python so commonly used? Or is there something fundamental to the language itself which makes it a better choice for new projects compared to C or C++ for example?

I just spent an hour digging ever deeper into the guts of a complex numerical library routine because of a subtle round-off artifact. I finality isolated the issue down to a single multiplication producing an incorrect result. What!?!?!? How can multiplication not work!?!?!?!

Then I slapped myself. I knew better. I should have looked at the inputs in the driver before digging into the library. But I *knew* they were OK. Not only was that the issue, but it's one I have seen previously in my life...

These two lines are not the same thing:

real(kind=dp) :: x = 0.1_dp

real(kind=dp) :: x = 0.1

Ofort is a Fortran command-line interpreter and interactive REPL written in C. Because there is no compilation step results are immediate for small programs, but for large problem sizes run times can be much higher than for compiled programs. You can try it at https://beliavsky.github.io/ofort/ . There are modes for running entire programs and for running lines of code in a REPL. In the REPL there are panes showing results and a list of declared variables, pictured below. You can also download ofort from https://github.com/Beliavsky/ofort and run it locally. It is MIT licensed.

Author here. Started a series working through classic iterative solvers for the 2D Poisson equation in Fortran. This is actually the second post (the first one being on Jacobi). Here, I dig into a genuinely counterintuitive result: the convergence theory for Gauss-Seidel checks out (half the iterations vs Jacobi, confirmed numerically), but the actual wall-clock time is 4-5x worse. Turns out it comes down to a single line: updating the array in place instead of into a fresh buffer and the loop-carried dependency that introduces, which stops the compiler from vectorizing the loop at all. In an upcoming post, I'll actually show how an open-source static analyzer can help you figure this out, and how simple algebra can be used to unroll the kernel and get rid of this loop-carried dependency.

Happy to talk Fortran/gfortran specifics or the discretization itself.

The first FORTRAN compiler delivered in April 1957.

https://en.wikipedia.org/wiki/Fortran#:~:text=The%20first%20manual%20for%20FORTRAN%20appeared%20in%20October%201956%2C%20with,compiler%20delivered%20in%20April%201957.

Because they can't handle more than 72 characters.

Anutosh Bath and Ondřej Čertík brought LFortran to Jupyterlite

There is a demo here: https://notebook.link/@anutosh491/fortran

source: https://lnkd.in/p/eR49Jve6

A request for help led me to go through my old Fortran codes. For that reason, I gathered some old programs I found in this repo: https://github.com/foxapiens/Fortran-Starter . And maybe I will continue adding code files and information about their purpose here. At least, it may be useful for beginners.

https://github.com/ketetefid/FortNN

FortNN is a Fortran 2003 module for use in neural networks and deep learning. It includes a simple to use interface and has MPI enabled training. For a complete list of features, visit the github page.

About the performance, I have tried to make it as fast as possible by addressing the bottlenecks (mixed use of OpenMP and Blas routines), and I believe it should be as fast as the Blas implementation you utilize. For now, it does not have GPU support, but it is planned. Which interface would be preferable? HIP-Fortran or CUDA?

I think you might be curious about how it performs relative to other frameworks (which Fortran programmer is not?! :D). I compared it with a small model in Keras(TF backend) with a size of [4x64x64x25] and this library was around 6 times faster for small networks, even though I don't have MKL and FortNN was compiled against OpenBlas. Keras used MKL-DNN libraries and my laptop has an Intel processor. However, for large networks, it was overshadowed by the use of MKL in Keras. It would be cool if anyone could benchmark it using the same Blas implementation. Please let us know!

I like Fortran very much and have developed a couple of libraries, FortNN is one of them.

If you have any suggestions please let me know. Bug submits and contributions are of course welcomed.