SIMD is actually underrated still. Programmers should always be thinking about it. It's a free 4x in a lot of cases
replyThe main problem with SIMD is it is a complex subject, even if not that good, autovectorization wins over what most common devs know about SIMD.
replyWell now you could in theory AI generate SIMD, which will be vibe coded, as those devs have no idea of its correctness.
I did see the article about that too. Don't know about "always" since there are applications like web backends where you're never going to add arrays of floats or something. Even if it's data science stuff, if that's in Python, Numpy is doing the SIMD for you.
replyI’m wondering if AI has made SIMD intrinsics much more approachable for many and so there are just more people working on abstractions for their workflow of choice right now. There’s probably a lot of code out there that could benefit from SIMD but the effort to actually use it was too high for the return.
replyMaybe things being on the front page reminds others? After seeing something, sometimes you can have ideas relating to it for a while.
replyYes, this kind of thing seems to happen quite often. Popular posts spurring further posts on a theme.
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