I agree with you that Fortran is running on more than just legacy here. At the same time, I also think Julia has caught up a lot as far as SIMD, multicore, MPI and GPU.

For SIMD, Chris Elrod's LoopVectorization.jl [1] is an (IMHO) incredibly impressive piece of work (which incidentally provides the foundation for I think the first pure Julia linear algebra library competitive with BLAS).

Multithreading is pretty easy with things like `@spawn`/`@sync` and `@threads for` in the base language, as well as super low-overhead multithreading from the Polyester.jl [2] package (which LoopVectorization also uses to provide a version its vectorization macro that'll also multithread your loops in addition to SIMD-vectorizing them).

MPI.jl [3] has been totally problem free for me, though I wouldn't be surprised if the Fortran bindings still have an edge somewhere, and Cuda.jl [4] seems to provide pretty seamless GPU support which should play nicely with MPI.jl's Cuda-aware MPI [5], but I don't work as much with GPUs myself.

[1] https://github.com/JuliaSIMD/LoopVectorization.jl

[2] https://github.com/JuliaSIMD/Polyester.jl

[3] https://github.com/JuliaParallel/MPI.jl

[4] https://github.com/JuliaGPU/CUDA.jl

[5] https://juliaparallel.github.io/MPI.jl/latest/usage/#CUDA-aw...