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This isn't true. Openblas and MKL are both C/C++ with assembly hardcoded microkernels. SciPy is in the process of removing the last of their Fortran because no one wants to maintain it, and newer methods in other languages are faster. Fortran hasn't been in the core of everything for decades.

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LAPACK is still Fortran, even in OpenBLAS where they only have f2c translated codes but hardly any assembly kernels.

For better or worse, Fortran is still a popular language to write clever PDE schemes in, as it maximizes "time to first, fast-enough-running code".

But for anything with a userbase of more than ~15 people, C/C++ are widely preferred.

Julia is starting to pick up steam here. It's a lot easier to write mixed precision algorithms in since the type system is pretty much designed for efficiently writing generic algorithms (and it doesn't hurt that Julia's ODE solvers are SOTA)

> Julia is starting to pick up steam here

First time I saw this claim was over 9 years ago.

We must disclose that @adgjlsfhk1 works for JuliaComputing. Sometimes they forget to do so on their own.

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