Chris Lattner

You look at a modern cell phone.

Modern cell phone, you've got CPUs.

And they're not just CPUs.

There's like big dot little CPUs.

And so there's multiple different kinds of CPUs that are kind of working together that are multi-core.

You've got GPUs.

You've got neural network accelerators.

You've got dedicated hardware blocks for media, so for video decode and JPEG decode and things like this.

And so you've got this massively complicated system.

And this isn't just cell phones.

Every laptop these days is doing the same thing.

And all of these blocks can run at the same time.

and need to be choreographed, right?

And so again, one of the cool things about machine learning is it's moving things to like data flow graphs and higher level of abstractions and tensors and these things that it doesn't specify, here's how to do the algorithm.

It gives the system a lot more flexibility in terms of how to translate or map or compile it onto the system that you have.

And so what you need, you know, the bottom is part of the layer there is a way for all these devices to talk to each other.

And so this is one thing that I'm very passionate about.

I mean, you know I'm a nerd.

But all these machines and all these systems are effectively parallel computers running at the same time, sending messages to each other.

And so they're all fully asynchronous.