Chris Lattner

If it's a matrix operation or something like that, you can say, okay, I'm going to carve it up into blocks.

I'm going to do those blocks in parallel.

And with 128 things that I'm running on, I want to cut it this way or that way or whatever.

And you can say, hey, go see which one's actually empirically better on the system.

And then the result of that, you cache for that system.

Yep.

You save it.

And so come back to twisting your compiler brain, right?

So not only does the compiler have an interpreter that's used to do metaprogramming, that compiler, that interpreter, that metaprogramming now has to actually take your code and go run it on a target machine.

See which one it likes the best and then stitch it in and then keep going, right?

So part of the compilation is machine-specific.

Yeah.

Well, so, I mean, this is an optional feature, right?

So you don't have to use it for everything, but yeah, if you, if you're, so one, one of, one of the things that we're in the quest of is ultimate performance.

Right.

And ultimate performance is important for a couple of reasons, right?

So if you're an enterprise, you're looking to save costs and compute and things like this, ultimate performance translates to, you know, fewer servers.

Like if you care about the environment, Hey,

better performance leads to more efficiency, right?

I mean, you could joke and say like, you know, Python's bad for the environment, right?