[20k]

The C++ way to do it currently would be:

    std::array<std::array<T, N>, M> data;

Which is contiguous

    int data[M][N]; 

also works fine and is contiguous in C++

Edit:

For the stack at least. On the heap, you'd need to use a single std::vector<int> and do the indices manually, or use mdspan

[uecker]

I does not work fine in C++ when N and M are not compile-time constants, which is basically always the case in any interesting numerical algorithm. Also not in Rust.

It works fine in C though, or FORTRAN, or Ada, or ALGOL 60, ...

[pjmlp]

Which is why std::mdspan exists, and std::linalg.

NVidia has pivoted to design CUDA hardware with focus on C++ back in , and seems to be doing quite well for them.

CppCon 2017: "Designing (New) C++ Hardware”

https://www.youtube.com/watch?v=86seb-iZCnI

They were also the ones sponsoring the ISO work on mdspan, while HPC research labs are pushing for linalg on top.

I would rather be using Ada today, but that isn't how the world moves.

[uecker]

I see that they spend time making their hardware run general software, but I can't see anything specific in GPU hardware to std::mdspan.

I respect Ada but I would not want to use it. But I have a choice between C++ with hmdspan and C99's arrays, I choose C99 any time.

[OneWingedShark]

Why is that? I find Ada much nicer than the C-languages when it comes to arrays: A'Range, A'Length, A'First, and A'Last are super-useful, as is the unconstrained array.

You can even use unconstrained arrays to provide the same functionality that Optional does in functional-programming, provided the element-type can be an element of an array:

    -- Here we define an index-type with one value.
    Subtype Boolean_Index is Boolean range True..True;
    -- And here we define an array indexed by it, but can also have length 0.
    Type Optional(Boolean_Index range <>) of Element;

And there you have the mechanism for Optional; just use "For Object of Optional_Array Loop" to enclose your operations and bam, it works perfectly.

[pjmlp]

I guess you aren't their target customer anyway, NVidia isn't that found of pure C code, with first class tooling for C, C++, Fortran, Python JIT, Ada and most recently Rust.

The std:mdspan proposal came from NVidia employees, alongside AMD and HPC research labs.

https://www.open-std.org/jtc1/sc22/wg21/docs/papers/2022/p00...

Yeah, I remember discussions on comp.lang.c calling programming with Ada, or even Modula-2, programming with straightjacket.

Meanwhile governments and national security bodies got another point of view.

You mean the C99 arrays Google paid the work to clean from the Linux kernel?

[uecker]

The thing is that I rewrite high performance numerical code on GPUs and the CUDA part is what sucks most. And the moment one uses templates, the compilation times make it insufferable. I really do not understand why people put up with this garbage. I am really looking forward to the day where I can remove CUDA from my projects and replace it with compiler-supported offloading is really

The kernel removed VLAs, I am more talking about vm types. But even for VLA - while I had a small role in that undertaking myself - I think it was a stupid mistake from a security point of view to remove VLAs from the kernel. Google pays for a lot of nonsense...