There is a colossal ergonomics difference if you compare using clang vs rust to writing a hashmap for example.
C compilers just have everything you can think of because everythin is first implemented there.
Using anything else just seems kind of pointless. I understand new languages do have benefits but I don't believe language matters that much really.
The person who writes that garbage pointer soup in C write Arc<> + multi threaded + macro garbage soup in Rust.
The rust compiler actually has similar things, but they're not available in stable builds. I suppose there are some issues if principle why not to include them in stable. E.g: https://doc.rust-lang.org/std/intrinsics/fn.prefetch_read_da...
Maybe some time in the future good acceptable abstractions will be conceived for them.. Perhaps using just using nightly builds for HPC is not that far out, though.
__builtin_assume is available on stable (though of course it's unsafe): https://doc.rust-lang.org/std/hint/fn.assert_unchecked.html
There's an open issue to stabilize the prefetch APIs: https://github.com/rust-lang/rust/issues/146941 As is usually the case when a minor standard-library feature remains unstable, the primary reason is that nobody has found the problem urgent enough to put in the required work to stabilize it. (There's an argument that this process is currently too inefficient, but that's a separate issue.) In the meantime, there are third-party libraries available that use inline assembly to offer this functionality, though this means they only support a couple of the most popular architectures.
As I understand it, the Fortran compiler just expects your code to respect the "restrictness", it doesn't enforce it.
Honestly, I still don't get why people have built up all these complex numerics frameworks in C and C++. Just use Fortran - it's built for exactly this usecase, and scientists will still be able to read your code without a CS degree. In fact, they'll probably be the ones writing it in the first place.
The rest are compiler extensions, but if you’re in the space you quickly learn that portability is valued far less than program optimization. Most of the point of your large calculations is the actual results themselves, not the code that got you there. The code needs to be correct and reproducible, but HPC folks (and grant funding agencies) don’t care if your Linux/amd64 program will run, unported, on Windows or on arm64. Or whether you’ve spent time making your kernels work with both rocm and cuda.