Very interesting. Does LFortran have the same internal array layout as the standard C runtime ?

A shared layout and a shared calling convention would be very nice.

Sorry about my naive question. Haven't touched Fortran directly in three decades I think.

EDIT: thanks for your reply. For some reason it has been flagged dead. So am responding here. You can mail dang hn at ycombinator dot co m about the flagging. He is very nice.

I would also like to know this. Fortran itself is column-major, so I would guess the internal layout isn't same for multi-dimensional arrays when compared to row-major C? I'm not sure how LFortran represents arrays internally though.

LFortran internally uses column-major, so interchanging data with C should be done carefully for multi-dimensional arrays. If row-major representation is highly needed feature, We can introduce a flag to do that. I'm not totally sure about that but it's doable under some conditions for sure.

We can internally do both row-major and column-major arrays. For Fortran we enable the column-major flag by default, but the door is open to have both at the same time.

Lots of scientific code in Fortran has sparse arrays, so a NxN array that only has values on 5 diagonals will store that as 5xN array to save memory allowing you to run a larger problem.

That's a very orthogonal issue.

Sparse arrays are supported on C libraries too. I have done my time with CSC and CSR even inside Python that called out to C libraries.

When you say you 'wrote this up', you mean you had an AI write (at least) chunks of it.

Indeed, just like I let my compiler write (at least) chunks of my AD logic. Not great when the tool becomes a leaky abstraction, but overall net positive don't you think?

Disagree; it irks me to read AI slop, and writing is meant to be persuasive and engaging to human readers.

Happy to take the blame for the lack of persuasian and engagement then :) Thanks for the feedback, although I’d like to believe there’s a way to have that cake and eat it too.

Very interesting stuff. How would I get GPU offload working? I have a rather complex scientific code I'm working on with JAX. Most of it can be expressed well with JAX's programming model, but the last 10% really sucks. It's still worth it so I don't have to mess around with offload onto whatever XPU flavor of the week. But going to C++ would really make my life easier, as long as I could use e.g. Kokkos.

You can write the cuda kernels and directly differentiate them using Enzyme. Last time I checked KOKKOS was still WIP: https://github.com/EnzymeAD/Enzyme/issues?q=kokkos

No idea lol. I assume it’s possible since both Enzyme and GPU programming are pervasive in Julia. Let us know if you end up trying.