mdspan VS spack

Why not use https://github.com/kokkos/mdspan though ?

It can be the base of whatever *you* write via bindings generators like pybind11. In that sense, the answer to your question is "however you like". For actual simulation code, you'll see a lot more legacy Fortran and C. That said, with things like mdspan maybe being standardized (proposal), efforts towards a standard linear algebra library, and the existence of ubiquitous HPC frameworks already having been written in C++, I would say it's only a matter of time before C++ accounts for an even bigger share of all HPC code.

I'm aware of only two relevant projects myself, I don't know much, came to reddit kind of by chance. One of the multi-dimensional array libraries proposed for potential standardisation, and a gnu machine learning library that was discontinued which could be worked off of. There's probably a lot more out there, but don't get distracted from making something awesome :)

As u/IyeOnline already made the important points about VLAs and std::vector, I would just add that you may find std::mdspan to be a helpful data structure. You can allocate 1d memory and give it a 2d shape of k with nice 2d indexing, eg auto& elem = mymdspan(row, col);.

Kokkos mdspan

The good thing here is that it heavily relies on mdpsan that is a multidimensional view that handle shape and strides. And kokkos provide a C++14 compatible implementation with a complete CUDA support.

> Are we talking about the same autotools?

Yes. Instead of figuring out how to do something particular with every single software package, I can do a --with-foo or --without-bar or --prefix=/opt/baz-1.2.3, and be fairly confident that it will work the way I want.

Certainly with package managers or (FreeBSD) Ports a lot is taken care of behind the scenes, but the above would also help the package/port maintainers as well. Lately I've been using Spack for special-needs compiles, but maintainer ease also helps there, but there are still cases one a 'fully manual' compile is still done.

> Suffice it to say, I prefer to work with handwritten makefiles.

Having everyone 'roll their own' system would probably be worse, because any "mysteriously failure" then has to be debugged specially for each project.

Have you tried Spack?

* https://spack.io

* https://spack.readthedocs.io/en/latest/

Well, good luck with that, cause it's broken.

Previous release miscompiled Python [1]

Current release miscompiles bison [2]

[1] https://github.com/spack/spack/issues/38724

[2] https://github.com/spack/spack/issues/37172#issuecomment-181...

gh is available via Homebrew, MacPorts, Conda, Spack, Webi, and as a…

> I can't count the number of times I've seen people say "md5 is fine for use case xyz" where in some counterintuitive way it wasn't fine.

I can count many more times that people told me that md5 was "broken" for file verification when, in fact, it never has been.

My main gripe with the article is that it portrays the entire legal profession as "backwards" and "deeply negligent" when they're not actually doing anything unsafe -- or even likely to be unsafe. And "tech" knows better. Much of tech, it would seem, has no idea about the use cases and why one might be safe or not. They just know something's "broken" -- so, clearly, we should update.

> Just use a safe one, even if you think you "don't need it".

Here's me switching 5,700 or so hashes from md5 to sha256 in 2019: https://github.com/spack/spack/pull/13185

Did I need it? No. Am I "compliant"? Yes.

Really, though, the main tangible benefit was that it saved me having to respond to questions and uninformed criticism from people unnecessarily worried about md5 checksums.

[1] https://github.com/spack/spack/blob/develop/var/spack/repos/...

In Spack [1] we can express all these constraints for the dependency solver, and we also try to always re-cythonize sources. The latter is because bundled cythonized files are sometimes forward incompatible with Python, so it's better to just regenerate those with an up to date cython.

[1] https://github.com/spack/spack/

You want to look at the tools used for HPC systems, these are generally very well tried and tested and can be setup for single machine usage. Remote access - we use ssh, but web interfaces such as Open On Demand exist - https://openondemand.org/. For managing Jobs, Slurm is currently the most popular option - https://slurm.schedmd.com/documentation.html. For a module system (to load software and libraries per user), Spack is a great - https://spack.io/. You might also want to consider containerisation options, https://apptainer.org/ is a good option.

git clone https://github.com/spack/spack.git ./spack/bin/spack install gcc