[lifthrasiir]

I think an even better advice is that everyone should know array programming, because you generally need that mindset for SIMD optimizations as (packed) SIMD-specific techniques are surprisingly rare. And array programming gives you a generally performant code even without SIMD because it is much easier to auto-vectorize.

[subset]

I'm no fan of closed-source languages, and lord knows MATLAB has its warts. But I can't deny that it was pretty seamless to write efficient vectorised code for numerical simulations at uni. I don't have much experience with it, but my understanding is that Julia is the closest thing to a more modern and expressive language that has similar vectorisation capabilities.

[disgruntledphd2]

R and Numpy are also pretty good at this (and Julia was inspired by both of these and Matlab).

In fact, I'm reasonably certain that R (known as S in the 70's) was the first real language designed around this concept.

[MarceColl]

I guess it depends on what real language means to you, but APL[1] existed before and it's fully array based.

[1]: https://en.wikipedia.org/wiki/APL_%28programming_language%29

[disgruntledphd2]

When I writing the comment I was like, maybe APL was first? Nah, no-one will claim that. So congratulations on nerd-sniping me ;)

[subset]

I learned R for statistical programming at uni, where there was less emphasis on vectorised computation, so it didn't jump to mind, but yes! I've never quite felt like numpy 'clicked' for me in the same way, though. It always felt a bit bolted on, which I suppose it is, as a library (though @ operator overloading etc makes things somewhat nicer now).

[macleginn]

Array programming where we compare first and look for the first failure later will not help much here if runs are short because by itself it doesn't give you early termination and you may spend a lot of time on wasted comparisons.