Chris Lattner

And what that does is that keeps out the little guys.

And sometimes they're not so little guys.

The big guys are also just not in those dominant positions.

And so what has been happening, and so a lot of you talk about the rise of new exotic crazy accelerators, is people have been trying to turn this from a let's go write lots of special kernels problem into a compiler problem.

And so we, and I contributed to this as well.

We, as an industry went into it, like, let's go make this compiler problem phase, let's call it.

And much of the industry is still in this phase, by the way.

So it's, I wouldn't say this phase is over.

And so the idea is to say, look, okay,

What a compiler does is it provides a much more general, extensible, hackable interface for dealing with the general case.

And so within machine learning algorithms, for example, people figured out that, hey, if I do a matrix multiplication and I do a ReLU,

right?

The classic activation function.

It is way faster to do one pass over the data and then do the ReLU on the output where I'm writing out the data, because ReLU is just a maximum operation, right?

Max is zero.

And so it's an amazing optimization.

Take Matmul ReLU, squish together in one operation.

Now we have Matmul ReLU.

Well, wait a second.

If I do that now, I just went from having, you know, two operators to three.