I personally learned a lot from cpp conference videos. I would highly recommmend it.

I'm not sure it's a "technique" but the general insight worth taking away from this is that compiler authors often write optimizers to recognize specific patterns so writing your code in a more idiomatic form increases the odds an optimizer will be able to optimize it.

In this specific instance, at the hardware level it helps to understand how the branch predictor works and why quicksort in particular is essentially the worst case for the branch predictor, and then you'll understand why the cmov/csel optimization is such a big win.

Casey Muratori's https://computerenhance.com is a good resource

Expose yourself to lower level technologies (compilers and optimization techniques, hardware history and design) and let curiosity guide you. Learn how to profile and analyze program performance.

Got to my first job out of college and they gave me core dumps and had me debug the kernel for a year. Not my kind of fun, but definitely got me skilled in the art of low level.

You could read compiler books, but I would actually recommend reading about CPUs and computer architecture directly. If you understand how the hardware works, then the optimizations are all very natural and fit into the picture perfectly, instead of being some arcane compiler magic that you have to take as a disconnected fact.

Personally I actually haven't read too many books on optimizations, I just absorbed information over years one thing at a time, but something like Computer Organization and Design is a pretty good intro to the low-level picture. If you want to drown in extremely dense technical topics that will give you a lot of jumping off points to search, read Agner Fog's microarchicture optimization guide (https://www.agner.org/optimize/). It won't tell you what LLVM is doing, but it'll tell you why it's doing it. Fair warning, it's dense and pretty dry.

Then it depends how interested you are in doing low-level nonsense. If you spend a lot of time writing performance oriented systems code, you'll come to use profiling tools that show you the assembly. If you stare at it long enough, you sometimes start to question why the compiler wrote it this way. And you're naturally led as you try to optimize your code to wonder how LLVM is coming up with this ASM that it spits out and why it sometimes gets it wrong.

There's nothing magical or that requires innate talent. You can learn all of this very naturally if you work close to the metal and take the time to question how the abstraction layer below you actually works. If you keep doing this, you eventually find out it's not that deep, it's just a lot of stuff accumulated over time, but none of it particularly difficult or inaccessible.

I did Nand2Tetris and then I understod why I need to vectorize. You get a view from nand-gate to software and get to see all the interfaces. Its a wonderful course.

I’m assuming he measured time by averaging on 100s of instances, or he maintained the exact same input for both versions of code. Would be a big oversight if not!

I hope the test data is the same when comparing the different runs. So the big o notation should be the same across different runs.

if you have decent (randomized) pivoting, you never hit the worst case or anything like it

Looking at big-O isn't very informative. We have plenty of statistical tools for telling whether there is an effect even with noisy data.