I actually had a fantastic experience with Fortran lately. I ported a compute kernel from python/numpy to Fortran 2018, partially due to the GIL and partly so I could use Intel's compiler. The performance improvement was tremendous. Several times faster per core, then multiplying further because I could take advantage of threading. In all, the 3 day project increased actual throughput 450x.

(I considered JAX, but the code in question was not amenable to a compute graph. Another option was to thread by fork, and use IPC.)

I liked the language itself more than expected. You have something like "generics" with tensors. Suppose you pass a parameter, N, and you also would like to pass a tensor, and you would like to specify the tensor's shape (N, N). You can do this; the parameter type constraints can reference other parameters.

Tensors and various operations are first-class types, so the compiler can optimise operations easily for the system you're building on. In my case, I got 80% improvement from ifx over gfortran.

Invocation from Python was basically the same as a C library. Both Python and Fortran have facilities for C interop, and Numpy can be asked to lay out tensors in a Fortran compatible way.

Part of what eased the port was that Numpy seems to be a kind of "Fortran wrapper". The ergonomics on tensor addressing, slicing and views is identical.