Measuring Gauss-Seidel loop-carried dependency and fixing it via loop unrolling

"... and this is the whole ballgame: ", "jump out immediately", "... and the idea is disarmingly simple", etc etc.

Written by Claude AI. Which is a problem because Claude text is riddled with engagement bait and filler, making it exhausting to read.

I doubt outside of a few exceptional cases that one is going to do better on CPU-bound problems than a well-written LAPACK implementation built for the architecture you intend to run on.

Maybe pedagogy was the point? I didn’t really get that from the article, maybe some intention was lost by filtering it through AI.

What language is that, and what does `0.25_dp` mean?

This article might be good but I always get scared when they use AI art for the header bc I wont know if I'm reading AI slop until i've invested a good amount of time into the article so I just avoid it all together.