vtt/episode_021_large.vtt

WEBVTT

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 The following is a conversation with Chris Latner.

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 Currently, he's a senior director

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 at Google working on several projects, including CPU, GPU,

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 TPU accelerators for TensorFlow, Swift for TensorFlow,

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 and all kinds of machine learning compiler magic

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 going on behind the scenes.

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 He's one of the top experts in the world

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 on compiler technologies, which means he deeply

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 understands the intricacies of how hardware and software come

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 together to create efficient code.

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 He created the LLVM compiler infrastructure project

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 and the Clang compiler.

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 He led major engineering efforts at Apple,

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 including the creation of the Swift programming language.

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 He also briefly spent time at Tesla

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 as vice president of Autopilot software

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 during the transition from Autopilot hardware 1

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 to hardware 2, when Tesla essentially

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 started from scratch to build an in house software

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 infrastructure for Autopilot.

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 I could have easily talked to Chris for many more hours.

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 Compiling code down across the levels of abstraction

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 is one of the most fundamental and fascinating aspects

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 of what computers do, and he is one of the world

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 experts in this process.

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 It's rigorous science, and it's messy, beautiful art.

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 This conversation is part of the Artificial Intelligence

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 podcast.

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 If you enjoy it, subscribe on YouTube, iTunes,

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 or simply connect with me on Twitter at Lex Friedman,

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 spelled F R I D.

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 And now, here's my conversation with Chris Ladner.

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 What was the first program you've ever written?

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 My first program.

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 Back, and when was it?

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 I think I started as a kid, and my parents

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 got a basic programming book.

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 And so when I started, it was typing out programs

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 from a book, and seeing how they worked,

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 and then typing them in wrong, and trying

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 to figure out why they were not working right,

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 that kind of stuff.

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 So BASIC, what was the first language

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 that you remember yourself maybe falling in love with,

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 like really connecting with?

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 I don't know.

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 I mean, I feel like I've learned a lot along the way,

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 and each of them have a different special thing

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 about them.

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 So I started in BASIC, and then went like GW BASIC,

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 which was the thing back in the DOS days,

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 and then upgraded to QBASIC, and eventually QuickBASIC,

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 which are all slightly more fancy versions of Microsoft

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 BASIC.

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 Made the jump to Pascal, and started

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 doing machine language programming and assembly

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 in Pascal, which was really cool.

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 Turbo Pascal was amazing for its day.

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 Eventually got into C, C++, and then kind of did

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 lots of other weird things.

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 I feel like you took the dark path, which is the,

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 you could have gone Lisp.

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 Yeah.

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 You could have gone higher level sort

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 of functional philosophical hippie route.

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 Instead, you went into like the dark arts of the C.

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 It was straight into the machine.

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 Straight to the machine.

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 So I started with BASIC, Pascal, and then Assembly,

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 and then wrote a lot of Assembly.

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 And I eventually did Smalltalk and other things like that.

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 But that was not the starting point.

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 But so what is this journey to C?

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 Is that in high school?

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 Is that in college?

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 That was in high school, yeah.

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 And then that was really about trying

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 to be able to do more powerful things than what Pascal could

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 do, and also to learn a different world.

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 So he was really confusing to me with pointers

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 and the syntax and everything, and it took a while.

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 But Pascal's much more principled in various ways.

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 C is more, I mean, it has its historical roots,

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 but it's not as easy to learn.

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 With pointers, there's this memory management thing

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 that you have to become conscious of.

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 Is that the first time you start to understand

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 that there's resources that you're supposed to manage?

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 Well, so you have that in Pascal as well.

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 But in Pascal, like the caret instead of the star,

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 there's some small differences like that.

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 But it's not about pointer arithmetic.

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 And in C, you end up thinking about how things get

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 laid out in memory a lot more.

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 And so in Pascal, you have allocating and deallocating

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 and owning the memory, but just the programs are simpler,

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 and you don't have to.

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 Well, for example, Pascal has a string type.

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 And so you can think about a string

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 instead of an array of characters

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 which are consecutive in memory.

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 So it's a little bit of a higher level abstraction.

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 So let's get into it.

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 Let's talk about LLVM, C lang, and compilers.

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 Sure.

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 So can you tell me first what LLVM and C lang are?

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 And how is it that you find yourself

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 the creator and lead developer, one

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 of the most powerful compiler optimization systems

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 in use today?

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 Sure.

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 So I guess they're different things.

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 So let's start with what is a compiler?

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 Is that a good place to start?

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 What are the phases of a compiler?

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 Where are the parts?

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 Yeah, what is it?

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 So what is even a compiler used for?

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 So the way I look at this is you have a two sided problem of you

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 have humans that need to write code.

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 And then you have machines that need to run

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 the program that the human wrote.

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 And for lots of reasons, the humans

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 don't want to be writing in binary

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 and want to think about every piece of hardware.

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 And so at the same time that you have lots of humans,

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 you also have lots of kinds of hardware.

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 And so compilers are the art of allowing

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 humans to think at a level of abstraction

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 that they want to think about.

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 And then get that program, get the thing that they wrote,

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 to run on a specific piece of hardware.

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 And the interesting and exciting part of all this

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 is that there's now lots of different kinds of hardware,

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 chips like x86 and PowerPC and ARM and things like that.

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 But also high performance accelerators

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 for machine learning and other things like that

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 are also just different kinds of hardware, GPUs.

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 These are new kinds of hardware.

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 And at the same time, on the programming side of it,

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 you have basic, you have C, you have JavaScript,

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 you have Python, you have Swift.

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 You have lots of other languages

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 that are all trying to talk to the human in a different way

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 to make them more expressive and capable and powerful.

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 And so compilers are the thing

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 that goes from one to the other.

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 End to end, from the very beginning to the very end.

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 End to end.

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 And so you go from what the human wrote

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 and programming languages end up being about

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 expressing intent, not just for the compiler

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 and the hardware, but the programming language's job

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 is really to capture an expression

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 of what the programmer wanted

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 that then can be maintained and adapted

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 and evolved by other humans,

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 as well as interpreted by the compiler.

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 So when you look at this problem,

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 you have, on the one hand, humans, which are complicated.

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 And you have hardware, which is complicated.

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 And so compilers typically work in multiple phases.

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 And so the software engineering challenge

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 that you have here is try to get maximum reuse

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 out of the amount of code that you write,

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 because these compilers are very complicated.

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 And so the way it typically works out

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 is that you have something called a front end or a parser

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 that is language specific.

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 And so you'll have a C parser, and that's what Clang is,

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 or C++ or JavaScript or Python or whatever.

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 That's the front end.

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 Then you'll have a middle part,

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 which is often the optimizer.

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 And then you'll have a late part,

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 which is hardware specific.

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 And so compilers end up,

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 there's many different layers often,

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 but these three big groups are very common in compilers.

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 And what LLVM is trying to do

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 is trying to standardize that middle and last part.

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 And so one of the cool things about LLVM

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 is that there are a lot of different languages

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 that compile through to it.

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 And so things like Swift, but also Julia, Rust,

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 Clang for C, C++, Subjective C,

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 like these are all very different languages

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 and they can all use the same optimization infrastructure,

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 which gets better performance,

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 and the same code generation infrastructure

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 for hardware support.

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 And so LLVM is really that layer that is common,

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 that all these different specific compilers can use.

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 And is it a standard, like a specification,

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 or is it literally an implementation?

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 It's an implementation.

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 And so I think there's a couple of different ways

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 of looking at it, right?

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 Because it depends on which angle you're looking at it from.

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 LLVM ends up being a bunch of code, okay?

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 So it's a bunch of code that people reuse

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 and they build compilers with.

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 We call it a compiler infrastructure

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 because it's kind of the underlying platform

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 that you build a concrete compiler on top of.

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 But it's also a community.

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 And the LLVM community is hundreds of people

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 that all collaborate.

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 And one of the most fascinating things about LLVM

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 over the course of time is that we've managed somehow

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 to successfully get harsh competitors

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 in the commercial space to collaborate

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 on shared infrastructure.

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 And so you have Google and Apple,

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 you have AMD and Intel,

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 you have Nvidia and AMD on the graphics side,

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 you have Cray and everybody else doing these things.

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 And all these companies are collaborating together

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 to make that shared infrastructure really, really great.

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 And they do this not out of the goodness of their heart,

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 but they do it because it's in their commercial interest

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 of having really great infrastructure

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 that they can build on top of

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 and facing the reality that it's so expensive

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 that no one company, even the big companies,

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 no one company really wants to implement it all themselves.

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 Expensive or difficult?

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 Both.

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 That's a great point because it's also about the skill sets.

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 And the skill sets are very hard to find.

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 How big is the LLVM?

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 It always seems like with open source projects,

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 the kind, an LLVM is open source?

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 Yes, it's open source.

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 It's about, it's 19 years old now, so it's fairly old.

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 It seems like the magic often happens

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 within a very small circle of people.

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 Yes.

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 At least their early birth and whatever.

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 Yes, so the LLVM came from a university project,

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 and so I was at the University of Illinois.

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 And there it was myself, my advisor,

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 and then a team of two or three research students

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 in the research group,

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 and we built many of the core pieces initially.

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 I then graduated and went to Apple,

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 and at Apple brought it to the products,

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 first in the OpenGL graphics stack,

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 but eventually to the C compiler realm,

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 and eventually built Clang,

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 and eventually built Swift and these things.

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 Along the way, building a team of people

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 that are really amazing compiler engineers

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 that helped build a lot of that.

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 And so as it was gaining momentum

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 and as Apple was using it, being open source and public

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 and encouraging contribution,

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 many others, for example, at Google,

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 came in and started contributing.

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 And in some cases, Google effectively owns Clang now

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 because it cares so much about C++

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 and the evolution of that ecosystem,

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 and so it's investing a lot in the C++ world

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 and the tooling and things like that.

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 And so likewise, NVIDIA cares a lot about CUDA.

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 And so CUDA uses Clang and uses LLVM

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 for graphics and GPGPU.

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 And so when you first started as a master's project,

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 I guess, did you think it was gonna go as far as it went?

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 Were you crazy ambitious about it?

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 No.

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 It seems like a really difficult undertaking, a brave one.

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 Yeah, no, no, no, it was nothing like that.

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 So my goal when I went to the University of Illinois

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 was to get in and out with a non thesis masters in a year

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 and get back to work.

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 So I was not planning to stay for five years

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 and build this massive infrastructure.

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 I got nerd sniped into staying.

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 And a lot of it was because LLVM was fun

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 and I was building cool stuff

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 and learning really interesting things

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 and facing both software engineering challenges,

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 but also learning how to work in a team

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 and things like that.

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 I had worked at many companies as interns before that,

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 but it was really a different thing

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 to have a team of people that are working together

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 and try and collaborate in version control.

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 And it was just a little bit different.

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 Like I said, I just talked to Don Knuth

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 and he believes that 2% of the world population

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 have something weird with their brain,

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 that they're geeks, they understand computers,

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 they're connected with computers.

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 He put it at exactly 2%.

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 Okay, so.

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 He's a specific guy.

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 It's very specific.

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 Well, he says, I can't prove it,

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 but it's very empirically there.

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 Is there something that attracts you

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 to the idea of optimizing code?

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 And he seems like that's one of the biggest,

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 coolest things about LLVM.

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 Yeah, that's one of the major things it does.

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 So I got into that because of a person, actually.

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 So when I was in my undergraduate,

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 I had an advisor, or a professor named Steve Vegdahl.

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 And he, I went to this little tiny private school.

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 There were like seven or nine people

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 in my computer science department,

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 students in my class.

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 So it was a very tiny, very small school.

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 It was kind of a wart on the side of the math department

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 kind of a thing at the time.

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 I think it's evolved a lot in the many years since then.

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 But Steve Vegdahl was a compiler guy.

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 And he was super passionate.

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 And his passion rubbed off on me.

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 And one of the things I like about compilers

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 is that they're large, complicated software pieces.

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 And so one of the culminating classes

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 that many computer science departments,

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 at least at the time, did was to say

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 that you would take algorithms and data structures

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 and all these core classes.

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 But then the compilers class was one of the last classes

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 you take because it pulls everything together.

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 And then you work on one piece of code

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 over the entire semester.

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 And so you keep building on your own work,

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 which is really interesting.

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 And it's also very challenging because in many classes,

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 if you don't get a project done, you just forget about it

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 and move on to the next one and get your B or whatever it is.

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 But here you have to live with the decisions you make

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 and continue to reinvest in it.

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 And I really like that.

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 And so I did an extra study project

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 with him the following semester.

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 And he was just really great.

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 And he was also a great mentor in a lot of ways.

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 And so from him and from his advice,

13:59.500 --> 14:01.380
 he encouraged me to go to graduate school.

14:01.380 --> 14:03.420
 I wasn't super excited about going to grad school.

14:03.420 --> 14:05.540
 I wanted the master's degree, but I

14:05.540 --> 14:08.940
 didn't want to be an academic.

14:08.940 --> 14:11.100
 But like I said, I kind of got tricked into saying

14:11.100 --> 14:12.180
 and was having a lot of fun.

14:12.180 --> 14:14.540
 And I definitely do not regret it.

14:14.540 --> 14:17.940
 What aspects of compilers were the things you connected with?

14:17.940 --> 14:22.100
 So LLVM, there's also the other part

14:22.100 --> 14:24.940
 that's really interesting if you're interested in languages

14:24.940 --> 14:29.620
 is parsing and just analyzing the language,

14:29.620 --> 14:31.220
 breaking it down, parsing, and so on.

14:31.220 --> 14:32.580
 Was that interesting to you, or were you

14:32.580 --> 14:34.060
 more interested in optimization?

14:34.060 --> 14:37.420
 For me, it was more so I'm not really a math person.

14:37.420 --> 14:38.180
 I could do math.

14:38.180 --> 14:41.540
 I understand some bits of it when I get into it.

14:41.540 --> 14:43.940
 But math is never the thing that attracted me.

14:43.940 --> 14:46.100
 And so a lot of the parser part of the compiler

14:46.100 --> 14:47.820
 has a lot of good formal theories

14:47.820 --> 14:50.060
 that Don, for example, knows quite well.

14:50.060 --> 14:51.540
 I'm still waiting for his book on that.

14:54.740 --> 14:57.900
 But I just like building a thing and seeing what it could do

14:57.900 --> 15:00.740
 and exploring and getting it to do more things

15:00.740 --> 15:04.020
 and then setting new goals and reaching for them.

15:04.020 --> 15:09.580
 And in the case of LLVM, when I started working on that,

15:09.580 --> 15:13.420
 my research advisor that I was working for was a compiler guy.

15:13.420 --> 15:15.620
 And so he and I specifically found each other

15:15.620 --> 15:16.940
 because we were both interested in compilers.

15:16.940 --> 15:19.500
 And so I started working with him and taking his class.

15:19.500 --> 15:21.580
 And a lot of LLVM initially was, it's

15:21.580 --> 15:24.380
 fun implementing all the standard algorithms and all

15:24.380 --> 15:26.380
 the things that people had been talking about

15:26.380 --> 15:27.220
 and were well known.

15:27.220 --> 15:30.620
 And they were in the curricula for advanced studies

15:30.620 --> 15:31.340
 and compilers.

15:31.340 --> 15:34.580
 And so just being able to build that was really fun.

15:34.580 --> 15:37.660
 And I was learning a lot by, instead of reading about it,

15:37.660 --> 15:38.660
 just building.

15:38.660 --> 15:40.220
 And so I enjoyed that.

15:40.220 --> 15:42.820
 So you said compilers are these complicated systems.

15:42.820 --> 15:46.180
 Can you even just with language try

15:46.180 --> 15:52.220
 to describe how you turn a C++ program into code?

15:52.220 --> 15:53.460
 Like, what are the hard parts?

15:53.460 --> 15:54.620
 Why is it so hard?

15:54.620 --> 15:57.020
 So I'll give you examples of the hard parts along the way.

15:57.020 --> 16:01.060
 So C++ is a very complicated programming language.

16:01.060 --> 16:03.500
 It's something like 1,400 pages in the spec.

16:03.500 --> 16:06.060
 So C++ by itself is crazy complicated.

16:06.060 --> 16:07.140
 Can we just pause?

16:07.140 --> 16:09.140
 What makes the language complicated in terms

16:09.140 --> 16:12.340
 of what's syntactically?

16:12.340 --> 16:14.300
 So it's what they call syntax.

16:14.300 --> 16:16.700
 So the actual how the characters are arranged, yes.

16:16.700 --> 16:20.020
 It's also semantics, how it behaves.

16:20.020 --> 16:21.900
 It's also, in the case of C++, there's

16:21.900 --> 16:23.380
 a huge amount of history.

16:23.380 --> 16:26.700
 C++ is built on top of C. You play that forward.

16:26.700 --> 16:29.860
 And then a bunch of suboptimal, in some cases, decisions

16:29.860 --> 16:31.620
 were made, and they compound.

16:31.620 --> 16:33.380
 And then more and more and more things

16:33.380 --> 16:36.980
 keep getting added to C++, and it will probably never stop.

16:36.980 --> 16:38.540
 But the language is very complicated

16:38.540 --> 16:39.540
 from that perspective.

16:39.540 --> 16:41.200
 And so the interactions between subsystems

16:41.200 --> 16:42.420
 is very complicated.

16:42.420 --> 16:43.580
 There's just a lot there.

16:43.580 --> 16:45.660
 And when you talk about the front end,

16:45.660 --> 16:47.060
 one of the major challenges, which

16:47.060 --> 16:51.140
 clang as a project, the C, C++ compiler that I built,

16:51.140 --> 16:54.480
 I and many people built, one of the challenges we took on

16:54.480 --> 16:57.780
 was we looked at GCC.

16:57.780 --> 17:02.540
 GCC, at the time, was a really good industry standardized

17:02.540 --> 17:05.260
 compiler that had really consolidated

17:05.260 --> 17:08.340
 a lot of the other compilers in the world and was a standard.

17:08.340 --> 17:10.620
 But it wasn't really great for research.

17:10.620 --> 17:12.580
 The design was very difficult to work with.

17:12.580 --> 17:16.620
 And it was full of global variables and other things

17:16.620 --> 17:18.540
 that made it very difficult to reuse in ways

17:18.540 --> 17:20.420
 that it wasn't originally designed for.

17:20.420 --> 17:22.740
 And so with clang, one of the things that we wanted to do

17:22.740 --> 17:25.500
 is push forward on better user interface,

17:25.500 --> 17:28.060
 so make error messages that are just better than GCC's.

17:28.060 --> 17:29.580
 And that's actually hard, because you

17:29.580 --> 17:32.780
 have to do a lot of bookkeeping in an efficient way

17:32.780 --> 17:33.700
 to be able to do that.

17:33.700 --> 17:35.180
 We want to make compile time better.

17:35.180 --> 17:37.500
 And so compile time is about making it efficient,

17:37.500 --> 17:38.900
 which is also really hard when you're keeping

17:38.900 --> 17:40.540
 track of extra information.

17:40.540 --> 17:43.380
 We wanted to make new tools available,

17:43.380 --> 17:46.380
 so refactoring tools and other analysis tools

17:46.380 --> 17:50.540
 that GCC never supported, also leveraging the extra information

17:50.540 --> 17:54.060
 we kept, but enabling those new classes of tools

17:54.060 --> 17:55.940
 that then get built into IDEs.

17:55.940 --> 17:59.380
 And so that's been one of the areas that clang has really

17:59.380 --> 18:01.300
 helped push the world forward in,

18:01.300 --> 18:05.060
 is in the tooling for C and C++ and things like that.

18:05.060 --> 18:07.500
 But C++ and the front end piece is complicated.

18:07.500 --> 18:09.000
 And you have to build syntax trees.

18:09.000 --> 18:11.340
 And you have to check every rule in the spec.

18:11.340 --> 18:14.020
 And you have to turn that back into an error message

18:14.020 --> 18:16.020
 to the human that the human can understand

18:16.020 --> 18:17.820
 when they do something wrong.

18:17.820 --> 18:20.740
 But then you start doing what's called lowering,

18:20.740 --> 18:23.060
 so going from C++ and the way that it represents

18:23.060 --> 18:24.980
 code down to the machine.

18:24.980 --> 18:27.380
 And when you do that, there's many different phases

18:27.380 --> 18:29.660
 you go through.

18:29.660 --> 18:33.020
 Often, there are, I think LLVM has something like 150

18:33.020 --> 18:36.260
 different what are called passes in the compiler

18:36.260 --> 18:38.780
 that the code passes through.

18:38.780 --> 18:41.860
 And these get organized in very complicated ways,

18:41.860 --> 18:44.360
 which affect the generated code and the performance

18:44.360 --> 18:45.980
 and compile time and many other things.

18:45.980 --> 18:47.300
 What are they passing through?

18:47.300 --> 18:53.980
 So after you do the clang parsing, what's the graph?

18:53.980 --> 18:54.900
 What does it look like?

18:54.900 --> 18:56.100
 What's the data structure here?

18:56.100 --> 18:59.060
 Yeah, so in the parser, it's usually a tree.

18:59.060 --> 19:01.100
 And it's called an abstract syntax tree.

19:01.100 --> 19:04.580
 And so the idea is you have a node for the plus

19:04.580 --> 19:06.820
 that the human wrote in their code.

19:06.820 --> 19:09.020
 Or the function call, you'll have a node for call

19:09.020 --> 19:11.900
 with the function that they call and the arguments they pass,

19:11.900 --> 19:14.460
 things like that.

19:14.460 --> 19:16.620
 This then gets lowered into what's

19:16.620 --> 19:18.620
 called an intermediate representation.

19:18.620 --> 19:22.100
 And intermediate representations are like LLVM has one.

19:22.100 --> 19:26.940
 And there, it's what's called a control flow graph.

19:26.940 --> 19:31.220
 And so you represent each operation in the program

19:31.220 --> 19:34.480
 as a very simple, like this is going to add two numbers.

19:34.480 --> 19:35.980
 This is going to multiply two things.

19:35.980 --> 19:37.460
 Maybe we'll do a call.

19:37.460 --> 19:40.260
 But then they get put in what are called blocks.

19:40.260 --> 19:43.580
 And so you get blocks of these straight line operations,

19:43.580 --> 19:45.340
 where instead of being nested like in a tree,

19:45.340 --> 19:46.900
 it's straight line operations.

19:46.900 --> 19:49.780
 And so there's a sequence and an ordering to these operations.

19:49.780 --> 19:51.820
 So within the block or outside the block?

19:51.820 --> 19:52.980
 That's within the block.

19:52.980 --> 19:54.980
 And so it's a straight line sequence of operations

19:54.980 --> 19:55.740
 within the block.

19:55.740 --> 19:58.980
 And then you have branches, like conditional branches,

19:58.980 --> 20:00.140
 between blocks.

20:00.140 --> 20:04.860
 And so when you write a loop, for example, in a syntax tree,

20:04.860 --> 20:08.060
 you would have a for node, like for a for statement

20:08.060 --> 20:10.540
 in a C like language, you'd have a for node.

20:10.540 --> 20:12.200
 And you have a pointer to the expression

20:12.200 --> 20:14.080
 for the initializer, a pointer to the expression

20:14.080 --> 20:16.040
 for the increment, a pointer to the expression

20:16.040 --> 20:18.900
 for the comparison, a pointer to the body.

20:18.900 --> 20:21.060
 And these are all nested underneath it.

20:21.060 --> 20:22.900
 In a control flow graph, you get a block

20:22.900 --> 20:26.820
 for the code that runs before the loop, so the initializer

20:26.820 --> 20:27.620
 code.

20:27.620 --> 20:30.340
 And you have a block for the body of the loop.

20:30.340 --> 20:33.780
 And so the body of the loop code goes in there,

20:33.780 --> 20:35.660
 but also the increment and other things like that.

20:35.660 --> 20:37.860
 And then you have a branch that goes back to the top

20:37.860 --> 20:39.900
 and a comparison and a branch that goes out.

20:39.900 --> 20:43.820
 And so it's more of an assembly level kind of representation.

20:43.820 --> 20:46.060
 But the nice thing about this level of representation

20:46.060 --> 20:48.700
 is it's much more language independent.

20:48.700 --> 20:51.900
 And so there's lots of different kinds of languages

20:51.900 --> 20:54.540
 with different kinds of, you know,

20:54.540 --> 20:56.840
 JavaScript has a lot of different ideas of what

20:56.840 --> 20:58.180
 is false, for example.

20:58.180 --> 21:00.780
 And all that can stay in the front end.

21:00.780 --> 21:04.220
 But then that middle part can be shared across all those.

21:04.220 --> 21:07.540
 How close is that intermediate representation

21:07.540 --> 21:10.620
 to neural networks, for example?

21:10.620 --> 21:13.540
 Are they, because everything you describe

21:13.540 --> 21:16.100
 is a kind of echoes of a neural network graph.

21:16.100 --> 21:18.940
 Are they neighbors or what?

21:18.940 --> 21:20.980
 They're quite different in details,

21:20.980 --> 21:22.520
 but they're very similar in idea.

21:22.520 --> 21:24.320
 So one of the things that neural networks do

21:24.320 --> 21:26.900
 is they learn representations for data

21:26.900 --> 21:29.140
 at different levels of abstraction.

21:29.140 --> 21:33.940
 And then they transform those through layers, right?

21:33.940 --> 21:35.660
 So the compiler does very similar things.

21:35.660 --> 21:37.320
 But one of the things the compiler does

21:37.320 --> 21:40.660
 is it has relatively few different representations.

21:40.660 --> 21:43.100
 Where a neural network often, as you get deeper, for example,

21:43.100 --> 21:44.820
 you get many different representations

21:44.820 --> 21:47.380
 in each layer or set of ops.

21:47.380 --> 21:50.260
 It's transforming between these different representations.

21:50.260 --> 21:53.100
 In a compiler, often you get one representation

21:53.100 --> 21:55.240
 and they do many transformations to it.

21:55.240 --> 21:59.540
 And these transformations are often applied iteratively.

21:59.540 --> 22:02.940
 And for programmers, there's familiar types of things.

22:02.940 --> 22:06.180
 For example, trying to find expressions inside of a loop

22:06.180 --> 22:08.540
 and pulling them out of a loop so they execute for times.

22:08.540 --> 22:10.740
 Or find redundant computation.

22:10.740 --> 22:15.380
 Or find constant folding or other simplifications,

22:15.380 --> 22:19.060
 turning two times x into x shift left by one.

22:19.060 --> 22:21.980
 And things like this are all the examples

22:21.980 --> 22:23.340
 of the things that happen.

22:23.340 --> 22:26.180
 But compilers end up getting a lot of theorem proving

22:26.180 --> 22:27.760
 and other kinds of algorithms that

22:27.760 --> 22:30.100
 try to find higher level properties of the program that

22:30.100 --> 22:32.280
 then can be used by the optimizer.

22:32.280 --> 22:32.780
 Cool.

22:32.780 --> 22:38.140
 So what's the biggest bang for the buck with optimization?

22:38.140 --> 22:38.640
 Today?

22:38.640 --> 22:39.140
 Yeah.

22:39.140 --> 22:40.900
 Well, no, not even today.

22:40.900 --> 22:42.900
 At the very beginning, the 80s, I don't know.

22:42.900 --> 22:44.300
 Yeah, so for the 80s, a lot of it

22:44.300 --> 22:46.420
 was things like register allocation.

22:46.420 --> 22:50.460
 So the idea of in a modern microprocessor,

22:50.460 --> 22:51.880
 what you'll end up having is you'll

22:51.880 --> 22:54.340
 end up having memory, which is relatively slow.

22:54.340 --> 22:57.060
 And then you have registers that are relatively fast.

22:57.060 --> 23:00.340
 But registers, you don't have very many of them.

23:00.340 --> 23:02.600
 And so when you're writing a bunch of code,

23:02.600 --> 23:04.180
 you're just saying, compute this,

23:04.180 --> 23:05.940
 put in a temporary variable, compute this, compute this,

23:05.940 --> 23:07.780
 compute this, put in a temporary variable.

23:07.780 --> 23:08.220
 I have a loop.

23:08.220 --> 23:09.780
 I have some other stuff going on.

23:09.780 --> 23:11.660
 Well, now you're running on an x86,

23:11.660 --> 23:13.900
 like a desktop PC or something.

23:13.900 --> 23:16.860
 Well, it only has, in some cases, some modes,

23:16.860 --> 23:18.700
 eight registers.

23:18.700 --> 23:21.620
 And so now the compiler has to choose what values get

23:21.620 --> 23:24.820
 put in what registers at what points in the program.

23:24.820 --> 23:26.580
 And this is actually a really big deal.

23:26.580 --> 23:29.500
 So if you think about, you have a loop, an inner loop

23:29.500 --> 23:31.620
 that executes millions of times maybe.

23:31.620 --> 23:33.620
 If you're doing loads and stores inside that loop,

23:33.620 --> 23:35.040
 then it's going to be really slow.

23:35.040 --> 23:37.740
 But if you can somehow fit all the values inside that loop

23:37.740 --> 23:40.180
 in registers, now it's really fast.

23:40.180 --> 23:43.020
 And so getting that right requires a lot of work,

23:43.020 --> 23:44.940
 because there's many different ways to do that.

23:44.940 --> 23:46.980
 And often what the compiler ends up doing

23:46.980 --> 23:48.840
 is it ends up thinking about things

23:48.840 --> 23:52.020
 in a different representation than what the human wrote.

23:52.020 --> 23:53.340
 You wrote into x.

23:53.340 --> 23:56.820
 Well, the compiler thinks about that as four different values,

23:56.820 --> 23:59.280
 each which have different lifetimes across the function

23:59.280 --> 24:00.420
 that it's in.

24:00.420 --> 24:03.180
 And each of those could be put in a register or memory

24:03.180 --> 24:06.140
 or different memory or maybe in some parts of the code

24:06.140 --> 24:08.360
 recomputed instead of stored and reloaded.

24:08.360 --> 24:10.700
 And there are many of these different kinds of techniques

24:10.700 --> 24:11.460
 that can be used.

24:11.460 --> 24:15.780
 So it's adding almost like a time dimension to it's

24:15.780 --> 24:18.300
 trying to optimize across time.

24:18.300 --> 24:20.340
 So it's considering when you're programming,

24:20.340 --> 24:21.860
 you're not thinking in that way.

24:21.860 --> 24:23.220
 Yeah, absolutely.

24:23.220 --> 24:27.100
 And so the RISC era made things.

24:27.100 --> 24:32.020
 So RISC chips, R I S C. The RISC chips,

24:32.020 --> 24:33.740
 as opposed to CISC chips.

24:33.740 --> 24:36.700
 The RISC chips made things more complicated for the compiler,

24:36.700 --> 24:40.660
 because what they ended up doing is ending up

24:40.660 --> 24:42.500
 adding pipelines to the processor, where

24:42.500 --> 24:45.020
 the processor can do more than one thing at a time.

24:45.020 --> 24:47.740
 But this means that the order of operations matters a lot.

24:47.740 --> 24:50.260
 So one of the classical compiler techniques that you use

24:50.260 --> 24:51.940
 is called scheduling.

24:51.940 --> 24:54.220
 And so moving the instructions around

24:54.220 --> 24:57.740
 so that the processor can keep its pipelines full instead

24:57.740 --> 24:59.220
 of stalling and getting blocked.

24:59.220 --> 25:01.180
 And so there's a lot of things like that that

25:01.180 --> 25:03.620
 are kind of bread and butter compiler techniques

25:03.620 --> 25:06.220
 that have been studied a lot over the course of decades now.

25:06.220 --> 25:08.540
 But the engineering side of making them real

25:08.540 --> 25:10.580
 is also still quite hard.

25:10.580 --> 25:12.460
 And you talk about machine learning.

25:12.460 --> 25:14.420
 This is a huge opportunity for machine learning,

25:14.420 --> 25:17.620
 because many of these algorithms are full of these

25:17.620 --> 25:19.300
 hokey, hand rolled heuristics, which

25:19.300 --> 25:21.820
 work well on specific benchmarks that don't generalize,

25:21.820 --> 25:23.940
 and full of magic numbers.

25:23.940 --> 25:26.620
 And I hear there's some techniques that

25:26.620 --> 25:28.060
 are good at handling that.

25:28.060 --> 25:32.220
 So what would be the, if you were to apply machine learning

25:32.220 --> 25:34.740
 to this, what's the thing you're trying to optimize?

25:34.740 --> 25:39.100
 Is it ultimately the running time?

25:39.100 --> 25:41.180
 You can pick your metric, and there's running time,

25:41.180 --> 25:43.900
 there's memory use, there's lots of different things

25:43.900 --> 25:44.940
 that you can optimize for.

25:44.940 --> 25:47.220
 Code size is another one that some people care about

25:47.220 --> 25:48.860
 in the embedded space.

25:48.860 --> 25:51.700
 Is this like the thinking into the future,

25:51.700 --> 25:54.500
 or has somebody actually been crazy enough

25:54.500 --> 25:58.060
 to try to have machine learning based parameter

25:58.060 --> 26:01.060
 tuning for the optimization of compilers?

26:01.060 --> 26:04.860
 So this is something that is, I would say, research right now.

26:04.860 --> 26:06.820
 There are a lot of research systems

26:06.820 --> 26:09.100
 that have been applying search in various forms.

26:09.100 --> 26:11.460
 And using reinforcement learning is one form,

26:11.460 --> 26:14.460
 but also brute force search has been tried for quite a while.

26:14.460 --> 26:18.180
 And usually, these are in small problem spaces.

26:18.180 --> 26:21.900
 So find the optimal way to code generate a matrix

26:21.900 --> 26:24.460
 multiply for a GPU, something like that,

26:24.460 --> 26:28.580
 where you say, there, there's a lot of design space of,

26:28.580 --> 26:29.900
 do you unroll loops a lot?

26:29.900 --> 26:32.660
 Do you execute multiple things in parallel?

26:32.660 --> 26:35.340
 And there's many different confounding factors here

26:35.340 --> 26:38.100
 because graphics cards have different numbers of threads

26:38.100 --> 26:41.020
 and registers and execution ports and memory bandwidth

26:41.020 --> 26:42.740
 and many different constraints that interact

26:42.740 --> 26:44.460
 in nonlinear ways.

26:44.460 --> 26:46.500
 And so search is very powerful for that.

26:46.500 --> 26:49.820
 And it gets used in certain ways,

26:49.820 --> 26:51.220
 but it's not very structured.

26:51.220 --> 26:52.620
 This is something that we need,

26:52.620 --> 26:54.500
 we as an industry need to fix.

26:54.500 --> 26:59.220
 So you said 80s, but like, so have there been like big jumps

26:59.220 --> 27:01.260
 in improvement and optimization?

27:01.260 --> 27:02.340
 Yeah.

27:02.340 --> 27:05.300
 Yeah, since then, what's the coolest thing?

27:05.300 --> 27:07.100
 It's largely been driven by hardware.

27:07.100 --> 27:09.860
 So, well, it's hardware and software.

27:09.860 --> 27:13.700
 So in the mid nineties, Java totally changed the world,

27:13.700 --> 27:14.540
 right?

27:14.540 --> 27:17.540
 And I'm still amazed by how much change was introduced

27:17.540 --> 27:19.340
 by the way or in a good way.

27:19.340 --> 27:22.420
 So like reflecting back, Java introduced things like,

27:22.420 --> 27:25.860
 all at once introduced things like JIT compilation.

27:25.860 --> 27:27.780
 None of these were novel, but it pulled it together

27:27.780 --> 27:30.580
 and made it mainstream and made people invest in it.

27:30.580 --> 27:33.620
 JIT compilation, garbage collection, portable code,

27:33.620 --> 27:36.620
 safe code, like memory safe code,

27:36.620 --> 27:41.380
 like a very dynamic dispatch execution model.

27:41.380 --> 27:42.620
 Like many of these things,

27:42.620 --> 27:44.060
 which had been done in research systems

27:44.060 --> 27:46.900
 and had been done in small ways in various places,

27:46.900 --> 27:47.980
 really came to the forefront,

27:47.980 --> 27:49.740
 really changed how things worked

27:49.740 --> 27:51.980
 and therefore changed the way people thought

27:51.980 --> 27:53.060
 about the problem.

27:53.060 --> 27:56.300
 JavaScript was another major world change

27:56.300 --> 27:57.740
 based on the way it works.

27:59.300 --> 28:01.300
 But also on the hardware side of things,

28:01.300 --> 28:06.300
 multi core and vector instructions really change

28:06.660 --> 28:08.380
 the problem space and are very,

28:09.460 --> 28:10.820
 they don't remove any of the problems

28:10.820 --> 28:12.380
 that compilers faced in the past,

28:12.380 --> 28:14.540
 but they add new kinds of problems

28:14.540 --> 28:16.380
 of how do you find enough work

28:16.380 --> 28:20.020
 to keep a four wide vector busy, right?

28:20.020 --> 28:22.660
 Or if you're doing a matrix multiplication,

28:22.660 --> 28:25.860
 how do you do different columns out of that matrix

28:25.860 --> 28:26.700
 at the same time?

28:26.700 --> 28:30.140
 And how do you maximally utilize the arithmetic compute

28:30.140 --> 28:31.460
 that one core has?

28:31.460 --> 28:33.500
 And then how do you take it to multiple cores?

28:33.500 --> 28:35.780
 How did the whole virtual machine thing change

28:35.780 --> 28:38.020
 the compilation pipeline?

28:38.020 --> 28:40.460
 Yeah, so what the Java virtual machine does

28:40.460 --> 28:44.180
 is it splits, just like I was talking about before,

28:44.180 --> 28:46.300
 where you have a front end that parses the code,

28:46.300 --> 28:48.020
 and then you have an intermediate representation

28:48.020 --> 28:49.460
 that gets transformed.

28:49.460 --> 28:51.020
 What Java did was they said,

28:51.020 --> 28:53.100
 we will parse the code and then compile to

28:53.100 --> 28:55.500
 what's known as Java byte code.

28:55.500 --> 28:58.580
 And that byte code is now a portable code representation

28:58.580 --> 29:02.420
 that is industry standard and locked down and can't change.

29:02.420 --> 29:05.100
 And then the back part of the compiler

29:05.100 --> 29:07.300
 that does optimization and code generation

29:07.300 --> 29:09.460
 can now be built by different vendors.

29:09.460 --> 29:10.300
 Okay.

29:10.300 --> 29:13.020
 And Java byte code can be shipped around across the wire.

29:13.020 --> 29:15.860
 It's memory safe and relatively trusted.

29:16.860 --> 29:18.660
 And because of that, it can run in the browser.

29:18.660 --> 29:20.540
 And that's why it runs in the browser, right?

29:20.540 --> 29:22.980
 And so that way you can be in,

29:22.980 --> 29:25.020
 again, back in the day, you would write a Java applet

29:25.020 --> 29:29.300
 and as a web developer, you'd build this mini app

29:29.300 --> 29:30.860
 that would run on a webpage.

29:30.860 --> 29:33.620
 Well, a user of that is running a web browser

29:33.620 --> 29:34.460
 on their computer.

29:34.460 --> 29:37.860
 You download that Java byte code, which can be trusted,

29:37.860 --> 29:41.060
 and then you do all the compiler stuff on your machine

29:41.060 --> 29:42.460
 so that you know that you trust that.

29:42.460 --> 29:44.060
 Now, is that a good idea or a bad idea?

29:44.060 --> 29:44.900
 It's a great idea.

29:44.900 --> 29:46.240
 I mean, it's a great idea for certain problems.

29:46.240 --> 29:49.540
 And I'm very much a believer that technology is itself

29:49.540 --> 29:50.520
 neither good nor bad.

29:50.520 --> 29:51.620
 It's how you apply it.

29:52.940 --> 29:54.660
 You know, this would be a very, very bad thing

29:54.660 --> 29:56.980
 for very low levels of the software stack.

29:56.980 --> 30:00.300
 But in terms of solving some of these software portability

30:00.300 --> 30:02.820
 and transparency, or portability problems,

30:02.820 --> 30:04.240
 I think it's been really good.

30:04.240 --> 30:06.600
 Now, Java ultimately didn't win out on the desktop.

30:06.600 --> 30:09.420
 And like, there are good reasons for that.

30:09.420 --> 30:13.220
 But it's been very successful on servers and in many places,

30:13.220 --> 30:16.300
 it's been a very successful thing over decades.

30:16.300 --> 30:21.300
 So what has been LLVMs and C langs improvements

30:21.300 --> 30:26.300
 and optimization that throughout its history,

30:28.640 --> 30:31.080
 what are some moments we had set back

30:31.080 --> 30:33.280
 and really proud of what's been accomplished?

30:33.280 --> 30:36.160
 Yeah, I think that the interesting thing about LLVM

30:36.160 --> 30:40.120
 is not the innovations and compiler research.

30:40.120 --> 30:41.900
 It has very good implementations

30:41.900 --> 30:44.000
 of various important algorithms, no doubt.

30:44.880 --> 30:48.280
 And a lot of really smart people have worked on it.

30:48.280 --> 30:50.560
 But I think that the thing that's most profound about LLVM

30:50.560 --> 30:53.840
 is that through standardization, it made things possible

30:53.840 --> 30:56.200
 that otherwise wouldn't have happened, okay?

30:56.200 --> 30:59.120
 And so interesting things that have happened with LLVM,

30:59.120 --> 31:01.260
 for example, Sony has picked up LLVM

31:01.260 --> 31:03.920
 and used it to do all the graphics compilation

31:03.920 --> 31:06.080
 in their movie production pipeline.

31:06.080 --> 31:07.920
 And so now they're able to have better special effects

31:07.920 --> 31:09.660
 because of LLVM.

31:09.660 --> 31:11.180
 That's kind of cool.

31:11.180 --> 31:13.000
 That's not what it was designed for, right?

31:13.000 --> 31:15.480
 But that's the sign of good infrastructure

31:15.480 --> 31:18.800
 when it can be used in ways it was never designed for

31:18.800 --> 31:20.960
 because it has good layering and software engineering

31:20.960 --> 31:23.440
 and it's composable and things like that.

31:23.440 --> 31:26.120
 Which is where, as you said, it differs from GCC.

31:26.120 --> 31:28.240
 Yes, GCC is also great in various ways,

31:28.240 --> 31:31.800
 but it's not as good as infrastructure technology.

31:31.800 --> 31:36.160
 It's really a C compiler, or it's a Fortran compiler.

31:36.160 --> 31:38.920
 It's not infrastructure in the same way.

31:38.920 --> 31:41.560
 Now you can tell I don't know what I'm talking about

31:41.560 --> 31:44.500
 because I keep saying C lang.

31:44.500 --> 31:48.080
 You can always tell when a person has clues,

31:48.080 --> 31:49.400
 by the way, to pronounce something.

31:49.400 --> 31:52.580
 I don't think, have I ever used C lang?

31:52.580 --> 31:54.120
 Entirely possible, have you?

31:54.120 --> 31:58.200
 Well, so you've used code, it's generated probably.

31:58.200 --> 32:01.760
 So C lang and LLVM are used to compile

32:01.760 --> 32:05.240
 all the apps on the iPhone effectively and the OSs.

32:05.240 --> 32:09.380
 It compiles Google's production server applications.

32:10.560 --> 32:14.840
 It's used to build GameCube games and PlayStation 4

32:14.840 --> 32:16.680
 and things like that.

32:16.680 --> 32:20.120
 So as a user, I have, but just everything I've done

32:20.120 --> 32:22.120
 that I experienced with Linux has been,

32:22.120 --> 32:23.560
 I believe, always GCC.

32:23.560 --> 32:26.520
 Yeah, I think Linux still defaults to GCC.

32:26.520 --> 32:27.800
 And is there a reason for that?

32:27.800 --> 32:29.440
 Or is it because, I mean, is there a reason for that?

32:29.440 --> 32:32.040
 It's a combination of technical and social reasons.

32:32.040 --> 32:35.960
 Many Linux developers do use C lang,

32:35.960 --> 32:39.720
 but the distributions, for lots of reasons,

32:40.560 --> 32:44.240
 use GCC historically, and they've not switched, yeah.

32:44.240 --> 32:46.640
 Because it's just anecdotally online,

32:46.640 --> 32:50.640
 it seems that LLVM has either reached the level of GCC

32:50.640 --> 32:53.520
 or superseded on different features or whatever.

32:53.520 --> 32:55.200
 The way I would say it is that they're so close,

32:55.200 --> 32:56.040
 it doesn't matter.

32:56.040 --> 32:56.860
 Yeah, exactly.

32:56.860 --> 32:58.160
 Like, they're slightly better in some ways,

32:58.160 --> 32:59.160
 slightly worse than otherwise,

32:59.160 --> 33:03.280
 but it doesn't actually really matter anymore, that level.

33:03.280 --> 33:06.280
 So in terms of optimization breakthroughs,

33:06.280 --> 33:09.160
 it's just been solid incremental work.

33:09.160 --> 33:12.520
 Yeah, yeah, which describes a lot of compilers.

33:12.520 --> 33:15.000
 The hard thing about compilers, in my experience,

33:15.000 --> 33:17.440
 is the engineering, the software engineering,

33:17.440 --> 33:20.160
 making it so that you can have hundreds of people

33:20.160 --> 33:23.600
 collaborating on really detailed, low level work

33:23.600 --> 33:25.400
 and scaling that.

33:25.400 --> 33:27.880
 And that's really hard.

33:27.880 --> 33:30.680
 And that's one of the things I think LLVM has done well.

33:32.160 --> 33:34.200
 And that kind of goes back to the original design goals

33:34.200 --> 33:37.200
 with it to be modular and things like that.

33:37.200 --> 33:38.880
 And incidentally, I don't want to take all the credit

33:38.880 --> 33:39.720
 for this, right?

33:39.720 --> 33:41.760
 I mean, some of the best parts about LLVM

33:41.760 --> 33:43.600
 is that it was designed to be modular.

33:43.600 --> 33:45.600
 And when I started, I would write, for example,

33:45.600 --> 33:48.500
 a register allocator, and then somebody much smarter than me

33:48.500 --> 33:50.720
 would come in and pull it out and replace it

33:50.720 --> 33:52.680
 with something else that they would come up with.

33:52.680 --> 33:55.200
 And because it's modular, they were able to do that.

33:55.200 --> 33:58.280
 And that's one of the challenges with GCC, for example,

33:58.280 --> 34:01.280
 is replacing subsystems is incredibly difficult.

34:01.280 --> 34:04.680
 It can be done, but it wasn't designed for that.

34:04.680 --> 34:06.080
 And that's one of the reasons that LLVM's been

34:06.080 --> 34:08.760
 very successful in the research world as well.

34:08.760 --> 34:12.960
 But in a community sense, Guido van Rossum, right,

34:12.960 --> 34:17.960
 from Python, just retired from, what is it?

34:18.480 --> 34:20.500
 Benevolent Dictator for Life, right?

34:20.500 --> 34:24.720
 So in managing this community of brilliant compiler folks,

34:24.720 --> 34:28.660
 is there, did it, for a time at least,

34:28.660 --> 34:31.480
 fall on you to approve things?

34:31.480 --> 34:34.240
 Oh yeah, so I mean, I still have something like

34:34.240 --> 34:37.980
 an order of magnitude more patches in LLVM

34:37.980 --> 34:42.760
 than anybody else, and many of those I wrote myself.

34:42.760 --> 34:47.760
 But you still write, I mean, you're still close to the,

34:47.880 --> 34:49.480
 to the, I don't know what the expression is,

34:49.480 --> 34:51.000
 to the metal, you still write code.

34:51.000 --> 34:52.220
 Yeah, I still write code.

34:52.220 --> 34:54.240
 Not as much as I was able to in grad school,

34:54.240 --> 34:56.760
 but that's an important part of my identity.

34:56.760 --> 34:58.880
 But the way that LLVM has worked over time

34:58.880 --> 35:01.360
 is that when I was a grad student, I could do all the work

35:01.360 --> 35:04.120
 and steer everything and review every patch

35:04.120 --> 35:05.800
 and make sure everything was done

35:05.800 --> 35:09.040
 exactly the way my opinionated sense

35:09.040 --> 35:11.760
 felt like it should be done, and that was fine.

35:11.760 --> 35:14.300
 But as things scale, you can't do that, right?

35:14.300 --> 35:17.100
 And so what ends up happening is LLVM

35:17.100 --> 35:20.520
 has a hierarchical system of what's called code owners.

35:20.520 --> 35:22.880
 These code owners are given the responsibility

35:22.880 --> 35:24.880
 not to do all the work,

35:24.880 --> 35:26.640
 not necessarily to review all the patches,

35:26.640 --> 35:28.800
 but to make sure that the patches do get reviewed

35:28.800 --> 35:30.320
 and make sure that the right thing's happening

35:30.320 --> 35:32.160
 architecturally in their area.

35:32.160 --> 35:36.720
 And so what you'll see is you'll see that, for example,

35:36.720 --> 35:38.560
 hardware manufacturers end up owning

35:38.560 --> 35:43.560
 the hardware specific parts of their hardware.

35:43.600 --> 35:44.520
 That's very common.

35:45.520 --> 35:47.720
 Leaders in the community that have done really good work

35:47.720 --> 35:50.880
 naturally become the de facto owner of something.

35:50.880 --> 35:53.400
 And then usually somebody else is like,

35:53.400 --> 35:55.520
 how about we make them the official code owner?

35:55.520 --> 35:58.600
 And then we'll have somebody to make sure

35:58.600 --> 36:00.320
 that all the patches get reviewed in a timely manner.

36:00.320 --> 36:02.080
 And then everybody's like, yes, that's obvious.

36:02.080 --> 36:03.240
 And then it happens, right?

36:03.240 --> 36:06.080
 And usually this is a very organic thing, which is great.

36:06.080 --> 36:08.740
 And so I'm nominally the top of that stack still,

36:08.740 --> 36:11.560
 but I don't spend a lot of time reviewing patches.

36:11.560 --> 36:16.520
 What I do is I help negotiate a lot of the technical

36:16.520 --> 36:18.040
 disagreements that end up happening

36:18.040 --> 36:19.660
 and making sure that the community as a whole

36:19.660 --> 36:22.040
 makes progress and is moving in the right direction

36:22.040 --> 36:23.920
 and doing that.

36:23.920 --> 36:28.240
 So we also started a nonprofit six years ago,

36:28.240 --> 36:30.840
 seven years ago, time's gone away.

36:30.840 --> 36:34.600
 And the LLVM Foundation nonprofit helps oversee

36:34.600 --> 36:36.440
 all the business sides of things and make sure

36:36.440 --> 36:38.800
 that the events that the LLVM community has

36:38.800 --> 36:41.600
 are funded and set up and run correctly

36:41.600 --> 36:42.800
 and stuff like that.

36:42.800 --> 36:45.160
 But the foundation is very much stays out

36:45.160 --> 36:49.060
 of the technical side of where the project is going.

36:49.060 --> 36:52.160
 Right, so it sounds like a lot of it is just organic.

36:53.160 --> 36:55.680
 Yeah, well, LLVM is almost 20 years old,

36:55.680 --> 36:56.600
 which is hard to believe.

36:56.600 --> 36:59.720
 Somebody pointed out to me recently that LLVM

36:59.720 --> 37:04.600
 is now older than GCC was when LLVM started, right?

37:04.600 --> 37:06.860
 So time has a way of getting away from you.

37:06.860 --> 37:10.400
 But the good thing about that is it has a really robust,

37:10.400 --> 37:13.520
 really amazing community of people that are

37:13.520 --> 37:15.460
 in their professional lives, spread across lots

37:15.460 --> 37:17.720
 of different companies, but it's a community

37:17.720 --> 37:21.120
 of people that are interested in similar kinds of problems

37:21.120 --> 37:23.680
 and have been working together effectively for years

37:23.680 --> 37:26.460
 and have a lot of trust and respect for each other.

37:26.460 --> 37:29.240
 And even if they don't always agree that we're able

37:29.240 --> 37:31.200
 to find a path forward.

37:31.200 --> 37:34.480
 So then in a slightly different flavor of effort,

37:34.480 --> 37:38.120
 you started at Apple in 2005 with the task

37:38.120 --> 37:41.800
 of making, I guess, LLVM production ready.

37:41.800 --> 37:44.640
 And then eventually 2013 through 2017,

37:44.640 --> 37:48.360
 leading the entire developer tools department.

37:48.360 --> 37:52.960
 We're talking about LLVM, Xcode, Objective C to Swift.

37:53.920 --> 37:58.580
 So in a quick overview of your time there,

37:58.580 --> 37:59.600
 what were the challenges?

37:59.600 --> 38:03.240
 First of all, leading such a huge group of developers,

38:03.240 --> 38:06.540
 what was the big motivator, dream, mission

38:06.540 --> 38:11.400
 behind creating Swift, the early birth of it

38:11.400 --> 38:13.400
 from Objective C and so on, and Xcode,

38:13.400 --> 38:14.240
 what are some challenges?

38:14.240 --> 38:15.900
 So these are different questions.

38:15.900 --> 38:19.720
 Yeah, I know, but I wanna talk about the other stuff too.

38:19.720 --> 38:21.240
 I'll stay on the technical side,

38:21.240 --> 38:24.480
 then we can talk about the big team pieces, if that's okay.

38:24.480 --> 38:29.060
 So it's to really oversimplify many years of hard work.

38:29.060 --> 38:32.440
 LLVM started, joined Apple, became a thing,

38:32.440 --> 38:34.600
 became successful and became deployed.

38:34.600 --> 38:35.960
 But then there's a question about

38:35.960 --> 38:38.880
 how do we actually parse the source code?

38:38.880 --> 38:40.320
 So LLVM is that back part,

38:40.320 --> 38:42.320
 the optimizer and the code generator.

38:42.320 --> 38:44.060
 And LLVM was really good for Apple

38:44.060 --> 38:46.060
 as it went through a couple of harder transitions.

38:46.060 --> 38:47.960
 I joined right at the time of the Intel transition,

38:47.960 --> 38:51.820
 for example, and 64 bit transitions,

38:51.820 --> 38:53.500
 and then the transition to ARM with the iPhone.

38:53.500 --> 38:54.720
 And so LLVM was very useful

38:54.720 --> 38:57.000
 for some of these kinds of things.

38:57.000 --> 38:58.480
 But at the same time, there's a lot of questions

38:58.480 --> 39:00.120
 around developer experience.

39:00.120 --> 39:01.960
 And so if you're a programmer pounding out

39:01.960 --> 39:03.460
 at the time Objective C code,

39:04.480 --> 39:06.520
 the error message you get, the compile time,

39:06.520 --> 39:09.760
 the turnaround cycle, the tooling and the IDE,

39:09.760 --> 39:13.000
 were not great, were not as good as they could be.

39:13.000 --> 39:18.000
 And so, as I occasionally do, I'm like,

39:18.080 --> 39:20.720
 well, okay, how hard is it to write a C compiler?

39:20.720 --> 39:22.560
 And so I'm not gonna commit to anybody,

39:22.560 --> 39:25.320
 I'm not gonna tell anybody, I'm just gonna just do it

39:25.320 --> 39:27.480
 nights and weekends and start working on it.

39:27.480 --> 39:29.740
 And then I built up in C,

39:29.740 --> 39:31.160
 there's this thing called the preprocessor,

39:31.160 --> 39:33.040
 which people don't like,

39:33.040 --> 39:35.480
 but it's actually really hard and complicated

39:35.480 --> 39:37.700
 and includes a bunch of really weird things

39:37.700 --> 39:39.280
 like trigraphs and other stuff like that

39:39.280 --> 39:40.960
 that are really nasty,

39:40.960 --> 39:44.080
 and it's the crux of a bunch of the performance issues

39:44.080 --> 39:45.640
 in the compiler.

39:45.640 --> 39:46.640
 Started working on the parser

39:46.640 --> 39:47.800
 and kind of got to the point where I'm like,

39:47.800 --> 39:49.880
 ah, you know what, we could actually do this.

39:49.880 --> 39:51.460
 Everybody's saying that this is impossible to do,

39:51.460 --> 39:53.960
 but it's actually just hard, it's not impossible.

39:53.960 --> 39:57.560
 And eventually told my manager about it,

39:57.560 --> 39:59.220
 and he's like, oh, wow, this is great,

39:59.220 --> 40:00.360
 we do need to solve this problem.

40:00.360 --> 40:02.560
 Oh, this is great, we can get you one other person

40:02.560 --> 40:04.440
 to work with you on this, you know?

40:04.440 --> 40:08.360
 And slowly a team is formed and it starts taking off.

40:08.360 --> 40:12.040
 And C++, for example, huge, complicated language.

40:12.040 --> 40:14.360
 People always assume that it's impossible to implement

40:14.360 --> 40:16.260
 and it's very nearly impossible,

40:16.260 --> 40:18.720
 but it's just really, really hard.

40:18.720 --> 40:20.840
 And the way to get there is to build it

40:20.840 --> 40:22.480
 one piece at a time incrementally.

40:22.480 --> 40:26.440
 And that was only possible because we were lucky

40:26.440 --> 40:28.160
 to hire some really exceptional engineers

40:28.160 --> 40:30.380
 that knew various parts of it very well

40:30.380 --> 40:32.680
 and could do great things.

40:32.680 --> 40:34.440
 Swift was kind of a similar thing.

40:34.440 --> 40:39.160
 So Swift came from, we were just finishing off

40:39.160 --> 40:42.600
 the first version of C++ support in Clang.

40:42.600 --> 40:47.260
 And C++ is a very formidable and very important language,

40:47.260 --> 40:49.280
 but it's also ugly in lots of ways.

40:49.280 --> 40:52.320
 And you can't influence C++ without thinking

40:52.320 --> 40:54.380
 there has to be a better thing, right?

40:54.380 --> 40:56.120
 And so I started working on Swift, again,

40:56.120 --> 40:58.560
 with no hope or ambition that would go anywhere,

40:58.560 --> 41:00.800
 just let's see what could be done,

41:00.800 --> 41:02.620
 let's play around with this thing.

41:02.620 --> 41:06.700
 It was me in my spare time, not telling anybody about it,

41:06.700 --> 41:09.420
 kind of a thing, and it made some good progress.

41:09.420 --> 41:11.260
 I'm like, actually, it would make sense to do this.

41:11.260 --> 41:14.800
 At the same time, I started talking with the senior VP

41:14.800 --> 41:17.720
 of software at the time, a guy named Bertrand Serlet.

41:17.720 --> 41:19.280
 And Bertrand was very encouraging.

41:19.280 --> 41:22.080
 He was like, well, let's have fun, let's talk about this.

41:22.080 --> 41:23.440
 And he was a little bit of a language guy,

41:23.440 --> 41:26.160
 and so he helped guide some of the early work

41:26.160 --> 41:30.420
 and encouraged me and got things off the ground.

41:30.420 --> 41:34.280
 And eventually told my manager and told other people,

41:34.280 --> 41:38.800
 and it started making progress.

41:38.800 --> 41:40.960
 The complicating thing with Swift

41:40.960 --> 41:43.880
 was that the idea of doing a new language

41:43.880 --> 41:47.840
 was not obvious to anybody, including myself.

41:47.840 --> 41:50.240
 And the tone at the time was that the iPhone

41:50.240 --> 41:53.440
 was successful because of Objective C.

41:53.440 --> 41:54.440
 Oh, interesting.

41:54.440 --> 41:57.160
 Not despite of or just because of.

41:57.160 --> 42:01.160
 And you have to understand that at the time,

42:01.160 --> 42:05.400
 Apple was hiring software people that loved Objective C.

42:05.400 --> 42:07.960
 And it wasn't that they came despite Objective C.

42:07.960 --> 42:10.240
 They loved Objective C, and that's why they got hired.

42:10.240 --> 42:13.080
 And so you had a software team that the leadership,

42:13.080 --> 42:15.200
 in many cases, went all the way back to Next,

42:15.200 --> 42:19.400
 where Objective C really became real.

42:19.400 --> 42:23.240
 And so they, quote unquote, grew up writing Objective C.

42:23.240 --> 42:25.720
 And many of the individual engineers

42:25.720 --> 42:28.360
 all were hired because they loved Objective C.

42:28.360 --> 42:30.560
 And so this notion of, OK, let's do new language

42:30.560 --> 42:34.120
 was kind of heretical in many ways.

42:34.120 --> 42:36.960
 Meanwhile, my sense was that the outside community wasn't really

42:36.960 --> 42:38.560
 in love with Objective C. Some people were,

42:38.560 --> 42:40.360
 and some of the most outspoken people were.

42:40.360 --> 42:42.620
 But other people were hitting challenges

42:42.620 --> 42:44.760
 because it has very sharp corners

42:44.760 --> 42:46.840
 and it's difficult to learn.

42:46.840 --> 42:50.160
 And so one of the challenges of making Swift happen that

42:50.160 --> 42:57.720
 was totally non technical is the social part of what do we do?

42:57.720 --> 43:00.320
 If we do a new language, which at Apple, many things

43:00.320 --> 43:02.240
 happen that don't ship.

43:02.240 --> 43:05.560
 So if we ship it, what is the metrics of success?

43:05.560 --> 43:06.400
 Why would we do this?

43:06.400 --> 43:08.060
 Why wouldn't we make Objective C better?

43:08.060 --> 43:10.160
 If Objective C has problems, let's file off

43:10.160 --> 43:12.160
 those rough corners and edges.

43:12.160 --> 43:15.640
 And one of the major things that became the reason to do this

43:15.640 --> 43:18.960
 was this notion of safety, memory safety.

43:18.960 --> 43:23.240
 And the way Objective C works is that a lot of the object system

43:23.240 --> 43:27.560
 and everything else is built on top of pointers in C.

43:27.560 --> 43:29.960
 Objective C is an extension on top of C.

43:29.960 --> 43:32.680
 And so pointers are unsafe.

43:32.680 --> 43:34.640
 And if you get rid of the pointers,

43:34.640 --> 43:36.480
 it's not Objective C anymore.

43:36.480 --> 43:39.080
 And so fundamentally, that was an issue

43:39.080 --> 43:42.200
 that you could not fix safety or memory safety

43:42.200 --> 43:45.640
 without fundamentally changing the language.

43:45.640 --> 43:49.920
 And so once we got through that part of the mental process

43:49.920 --> 43:53.200
 and the thought process, it became a design process

43:53.200 --> 43:55.400
 of saying, OK, well, if we're going to do something new,

43:55.400 --> 43:56.280
 what is good?

43:56.280 --> 43:57.400
 How do we think about this?

43:57.400 --> 43:58.200
 And what do we like?

43:58.200 --> 44:00.040
 And what are we looking for?

44:00.040 --> 44:02.440
 And that was a very different phase of it.

44:02.440 --> 44:05.960
 So what are some design choices early on in Swift?

44:05.960 --> 44:10.120
 Like we're talking about braces, are you

44:10.120 --> 44:13.240
 making a typed language or not, all those kinds of things.

44:13.240 --> 44:16.040
 Yeah, so some of those were obvious given the context.

44:16.040 --> 44:17.800
 So a typed language, for example,

44:17.800 --> 44:19.200
 Objective C is a typed language.

44:19.200 --> 44:22.480
 And going with an untyped language

44:22.480 --> 44:24.320
 wasn't really seriously considered.

44:24.320 --> 44:26.000
 We wanted the performance, and we

44:26.000 --> 44:27.680
 wanted refactoring tools and other things

44:27.680 --> 44:29.600
 like that that go with typed languages.

44:29.600 --> 44:31.440
 Quick, dumb question.

44:31.440 --> 44:34.600
 Was it obvious, I think this would be a dumb question,

44:34.600 --> 44:36.360
 but was it obvious that the language

44:36.360 --> 44:40.120
 has to be a compiled language?

44:40.120 --> 44:42.080
 Yes, that's not a dumb question.

44:42.080 --> 44:44.520
 Earlier, I think late 90s, Apple had seriously

44:44.520 --> 44:49.000
 considered moving its development experience to Java.

44:49.000 --> 44:53.160
 But Swift started in 2010, which was several years

44:53.160 --> 44:53.880
 after the iPhone.

44:53.880 --> 44:55.380
 It was when the iPhone was definitely

44:55.380 --> 44:56.640
 on an upward trajectory.

44:56.640 --> 44:58.760
 And the iPhone was still extremely,

44:58.760 --> 45:01.800
 and is still a bit memory constrained.

45:01.800 --> 45:04.440
 And so being able to compile the code

45:04.440 --> 45:08.160
 and then ship it and then having standalone code that

45:08.160 --> 45:11.320
 is not JIT compiled is a very big deal

45:11.320 --> 45:15.200
 and is very much part of the Apple value system.

45:15.200 --> 45:17.480
 Now, JavaScript's also a thing.

45:17.480 --> 45:19.360
 I mean, it's not that this is exclusive,

45:19.360 --> 45:21.640
 and technologies are good depending

45:21.640 --> 45:23.880
 on how they're applied.

45:23.880 --> 45:26.600
 But in the design of Swift, saying,

45:26.600 --> 45:28.320
 how can we make Objective C better?

45:28.320 --> 45:29.760
 Objective C is statically compiled,

45:29.760 --> 45:32.520
 and that was the contiguous, natural thing to do.

45:32.520 --> 45:35.360
 Just skip ahead a little bit, and we'll go right back.

45:35.360 --> 45:40.040
 Just as a question, as you think about today in 2019

45:40.040 --> 45:42.400
 in your work at Google, TensorFlow and so on,

45:42.400 --> 45:48.600
 is, again, compilations, static compilation still

45:48.600 --> 45:49.460
 the right thing?

45:49.460 --> 45:52.000
 Yeah, so the funny thing after working

45:52.000 --> 45:55.880
 on compilers for a really long time is that,

45:55.880 --> 45:59.040
 and this is one of the things that LLVM has helped with,

45:59.040 --> 46:01.440
 is that I don't look at compilations

46:01.440 --> 46:05.240
 being static or dynamic or interpreted or not.

46:05.240 --> 46:07.680
 This is a spectrum.

46:07.680 --> 46:09.140
 And one of the cool things about Swift

46:09.140 --> 46:12.160
 is that Swift is not just statically compiled.

46:12.160 --> 46:14.080
 It's actually dynamically compiled as well,

46:14.080 --> 46:15.320
 and it can also be interpreted.

46:15.320 --> 46:17.440
 Though, nobody's actually done that.

46:17.440 --> 46:20.400
 And so what ends up happening when

46:20.400 --> 46:24.080
 you use Swift in a workbook, for example in Colab or in Jupyter,

46:24.080 --> 46:26.360
 is it's actually dynamically compiling the statements

46:26.360 --> 46:28.160
 as you execute them.

46:28.160 --> 46:32.840
 And so this gets back to the software engineering problems,

46:32.840 --> 46:34.960
 where if you layer the stack properly,

46:34.960 --> 46:37.320
 you can actually completely change

46:37.320 --> 46:39.360
 how and when things get compiled because you

46:39.360 --> 46:41.120
 have the right abstractions there.

46:41.120 --> 46:44.800
 And so the way that a Colab workbook works with Swift

46:44.800 --> 46:47.720
 is that when you start typing into it,

46:47.720 --> 46:50.280
 it creates a process, a Unix process.

46:50.280 --> 46:52.160
 And then each line of code you type in,

46:52.160 --> 46:56.120
 it compiles it through the Swift compiler, the front end part,

46:56.120 --> 46:58.360
 and then sends it through the optimizer,

46:58.360 --> 47:01.120
 JIT compiles machine code, and then

47:01.120 --> 47:03.800
 injects it into that process.

47:03.800 --> 47:05.400
 And so as you're typing new stuff,

47:05.400 --> 47:09.360
 it's like squirting in new code and overwriting and replacing

47:09.360 --> 47:11.200
 and updating code in place.

47:11.200 --> 47:13.680
 And the fact that it can do this is not an accident.

47:13.680 --> 47:15.560
 Swift was designed for this.

47:15.560 --> 47:18.120
 But it's an important part of how the language was set up

47:18.120 --> 47:21.320
 and how it's layered, and this is a nonobvious piece.

47:21.320 --> 47:23.160
 And one of the things with Swift that

47:23.160 --> 47:25.880
 was, for me, a very strong design point

47:25.880 --> 47:29.640
 is to make it so that you can learn it very quickly.

47:29.640 --> 47:31.880
 And so from a language design perspective,

47:31.880 --> 47:33.340
 the thing that I always come back to

47:33.340 --> 47:36.440
 is this UI principle of progressive disclosure

47:36.440 --> 47:37.960
 of complexity.

47:37.960 --> 47:41.680
 And so in Swift, you can start by saying print, quote,

47:41.680 --> 47:44.040
 hello world, quote.

47:44.040 --> 47:47.160
 And there's no slash n, just like Python, one line of code,

47:47.160 --> 47:51.520
 no main, no header files, no public static class void,

47:51.520 --> 47:55.640
 blah, blah, blah, string like Java has, one line of code.

47:55.640 --> 47:58.400
 And you can teach that, and it works great.

47:58.400 --> 48:00.400
 Then you can say, well, let's introduce variables.

48:00.400 --> 48:02.400
 And so you can declare a variable with var.

48:02.400 --> 48:03.780
 So var x equals 4.

48:03.780 --> 48:04.700
 What is a variable?

48:04.700 --> 48:06.280
 You can use x, x plus 1.

48:06.280 --> 48:07.600
 This is what it means.

48:07.600 --> 48:09.520
 Then you can say, well, how about control flow?

48:09.520 --> 48:10.860
 Well, this is what an if statement is.

48:10.860 --> 48:12.280
 This is what a for statement is.

48:12.280 --> 48:15.280
 This is what a while statement is.

48:15.280 --> 48:17.280
 Then you can say, let's introduce functions.

48:17.280 --> 48:20.020
 And many languages like Python have

48:20.020 --> 48:22.820
 had this kind of notion of let's introduce small things,

48:22.820 --> 48:24.400
 and then you can add complexity.

48:24.400 --> 48:25.760
 Then you can introduce classes.

48:25.760 --> 48:28.040
 And then you can add generics, in the case of Swift.

48:28.040 --> 48:29.520
 And then you can build in modules

48:29.520 --> 48:32.200
 and build out in terms of the things that you're expressing.

48:32.200 --> 48:35.800
 But this is not very typical for compiled languages.

48:35.800 --> 48:38.000
 And so this was a very strong design point,

48:38.000 --> 48:40.960
 and one of the reasons that Swift, in general,

48:40.960 --> 48:43.480
 is designed with this factoring of complexity in mind

48:43.480 --> 48:46.440
 so that the language can express powerful things.

48:46.440 --> 48:49.280
 You can write firmware in Swift if you want to.

48:49.280 --> 48:51.900
 But it has a very high level feel,

48:51.900 --> 48:55.200
 which is really this perfect blend, because often you

48:55.200 --> 48:57.520
 have very advanced library writers that

48:57.520 --> 49:00.520
 want to be able to use the nitty gritty details.

49:00.520 --> 49:02.960
 But then other people just want to use the libraries

49:02.960 --> 49:04.880
 and work at a higher abstraction level.

49:04.880 --> 49:07.240
 It's kind of cool that I saw that you can just

49:07.240 --> 49:09.240
 interoperability.

49:09.240 --> 49:11.320
 I don't think I pronounced that word enough.

49:11.320 --> 49:14.960
 But you can just drag in Python.

49:14.960 --> 49:16.000
 It's just strange.

49:16.000 --> 49:19.640
 You can import, like I saw this in the demo.

49:19.640 --> 49:21.280
 How do you make that happen?

49:21.280 --> 49:23.120
 What's up with that?

49:23.120 --> 49:25.560
 Is that as easy as it looks, or is it?

49:25.560 --> 49:27.000
 Yes, as easy as it looks.

49:27.000 --> 49:29.600
 That's not a stage magic hack or anything like that.

49:29.600 --> 49:31.400
 I don't mean from the user perspective.

49:31.400 --> 49:34.120
 I mean from the implementation perspective to make it happen.

49:34.120 --> 49:37.000
 So it's easy once all the pieces are in place.

49:37.000 --> 49:39.280
 The way it works, so if you think about a dynamically typed

49:39.280 --> 49:41.480
 language like Python, you can think about it

49:41.480 --> 49:42.360
 in two different ways.

49:42.360 --> 49:45.800
 You can say it has no types, which

49:45.800 --> 49:47.480
 is what most people would say.

49:47.480 --> 49:50.400
 Or you can say it has one type.

49:50.400 --> 49:53.320
 And you can say it has one type, and it's the Python object.

49:53.320 --> 49:55.000
 And the Python object gets passed around.

49:55.000 --> 49:58.200
 And because there's only one type, it's implicit.

49:58.200 --> 50:00.880
 And so what happens with Swift and Python talking

50:00.880 --> 50:02.760
 to each other, Swift has lots of types.

50:02.760 --> 50:05.840
 It has arrays, and it has strings, and all classes,

50:05.840 --> 50:07.000
 and that kind of stuff.

50:07.000 --> 50:11.120
 But it now has a Python object type.

50:11.120 --> 50:12.720
 So there is one Python object type.

50:12.720 --> 50:16.440
 And so when you say import NumPy, what you get

50:16.440 --> 50:19.840
 is a Python object, which is the NumPy module.

50:19.840 --> 50:21.960
 And then you say np.array.

50:21.960 --> 50:24.960
 It says, OK, hey, Python object, I have no idea what you are.

50:24.960 --> 50:27.280
 Give me your array member.

50:27.280 --> 50:27.960
 OK, cool.

50:27.960 --> 50:31.160
 And it just uses dynamic stuff, talks to the Python interpreter,

50:31.160 --> 50:33.680
 and says, hey, Python, what's the.array member

50:33.680 --> 50:35.720
 in that Python object?

50:35.720 --> 50:37.400
 It gives you back another Python object.

50:37.400 --> 50:40.040
 And now you say parentheses for the call and the arguments

50:40.040 --> 50:40.920
 you're going to pass.

50:40.920 --> 50:43.520
 And so then it says, hey, a Python object

50:43.520 --> 50:47.840
 that is the result of np.array, call with these arguments.

50:47.840 --> 50:50.320
 Again, calling into the Python interpreter to do that work.

50:50.320 --> 50:53.680
 And so right now, this is all really simple.

50:53.680 --> 50:55.960
 And if you dive into the code, what you'll see

50:55.960 --> 50:58.440
 is that the Python module in Swift

50:58.440 --> 51:01.360
 is something like 1,200 lines of code or something.

51:01.360 --> 51:02.400
 It's written in pure Swift.

51:02.400 --> 51:03.560
 It's super simple.

51:03.560 --> 51:06.560
 And it's built on top of the C interoperability

51:06.560 --> 51:09.520
 because it just talks to the Python interpreter.

51:09.520 --> 51:11.080
 But making that possible required

51:11.080 --> 51:13.480
 us to add two major language features to Swift

51:13.480 --> 51:15.400
 to be able to express these dynamic calls

51:15.400 --> 51:17.240
 and the dynamic member lookups.

51:17.240 --> 51:19.480
 And so what we've done over the last year

51:19.480 --> 51:23.960
 is we've proposed, implement, standardized, and contributed

51:23.960 --> 51:26.160
 new language features to the Swift language

51:26.160 --> 51:29.560
 in order to make it so it is really trivial.

51:29.560 --> 51:31.320
 And this is one of the things about Swift

51:31.320 --> 51:35.000
 that is critical to the Swift for TensorFlow work, which

51:35.000 --> 51:37.200
 is that we can actually add new language features.

51:37.200 --> 51:39.160
 And the bar for adding those is high,

51:39.160 --> 51:42.280
 but it's what makes it possible.

51:42.280 --> 51:45.240
 So you're now at Google doing incredible work

51:45.240 --> 51:47.680
 on several things, including TensorFlow.

51:47.680 --> 51:53.080
 So TensorFlow 2.0 or whatever leading up to 2.0 has,

51:53.080 --> 51:56.840
 by default, in 2.0, has eager execution.

51:56.840 --> 52:00.520
 And yet, in order to make code optimized for GPU or TPU

52:00.520 --> 52:04.120
 or some of these systems, computation

52:04.120 --> 52:06.000
 needs to be converted to a graph.

52:06.000 --> 52:07.440
 So what's that process like?

52:07.440 --> 52:08.960
 What are the challenges there?

52:08.960 --> 52:11.720
 Yeah, so I am tangentially involved in this.

52:11.720 --> 52:15.280
 But the way that it works with Autograph

52:15.280 --> 52:21.600
 is that you mark your function with a decorator.

52:21.600 --> 52:24.280
 And when Python calls it, that decorator is invoked.

52:24.280 --> 52:28.240
 And then it says, before I call this function,

52:28.240 --> 52:29.480
 you can transform it.

52:29.480 --> 52:32.400
 And so the way Autograph works is, as far as I understand,

52:32.400 --> 52:34.440
 is it actually uses the Python parser

52:34.440 --> 52:37.160
 to go parse that, turn it into a syntax tree,

52:37.160 --> 52:39.400
 and now apply compiler techniques to, again,

52:39.400 --> 52:42.320
 transform this down into TensorFlow graphs.

52:42.320 --> 52:44.920
 And so you can think of it as saying, hey,

52:44.920 --> 52:45.880
 I have an if statement.

52:45.880 --> 52:48.360
 I'm going to create an if node in the graph,

52:48.360 --> 52:51.080
 like you say tf.cond.

52:51.080 --> 52:53.040
 You have a multiply.

52:53.040 --> 52:55.320
 Well, I'll turn that into a multiply node in the graph.

52:55.320 --> 52:57.760
 And it becomes this tree transformation.

52:57.760 --> 53:00.480
 So where does the Swift for TensorFlow

53:00.480 --> 53:04.960
 come in, which is parallels?

53:04.960 --> 53:06.960
 For one, Swift is an interface.

53:06.960 --> 53:09.200
 Like, Python is an interface to TensorFlow.

53:09.200 --> 53:11.760
 But it seems like there's a lot more going on in just

53:11.760 --> 53:13.120
 a different language interface.

53:13.120 --> 53:15.960
 There's optimization methodology.

53:15.960 --> 53:17.920
 So the TensorFlow world has a couple

53:17.920 --> 53:21.240
 of different what I'd call front end technologies.

53:21.240 --> 53:25.240
 And so Swift and Python and Go and Rust and Julia

53:25.240 --> 53:29.320
 and all these things share the TensorFlow graphs

53:29.320 --> 53:32.760
 and all the runtime and everything that's later.

53:32.760 --> 53:36.640
 And so Swift for TensorFlow is merely another front end

53:36.640 --> 53:40.640
 for TensorFlow, just like any of these other systems are.

53:40.640 --> 53:43.080
 There's a major difference between, I would say,

53:43.080 --> 53:44.600
 three camps of technologies here.

53:44.600 --> 53:46.880
 There's Python, which is a special case,

53:46.880 --> 53:49.160
 because the vast majority of the community effort

53:49.160 --> 53:51.120
 is going to the Python interface.

53:51.120 --> 53:52.920
 And Python has its own approaches

53:52.920 --> 53:54.480
 for automatic differentiation.

53:54.480 --> 53:58.160
 It has its own APIs and all this kind of stuff.

53:58.160 --> 54:00.320
 There's Swift, which I'll talk about in a second.

54:00.320 --> 54:02.040
 And then there's kind of everything else.

54:02.040 --> 54:05.400
 And so the everything else are effectively language bindings.

54:05.400 --> 54:07.960
 So they call into the TensorFlow runtime,

54:07.960 --> 54:10.920
 but they usually don't have automatic differentiation

54:10.920 --> 54:14.560
 or they usually don't provide anything other than APIs

54:14.560 --> 54:16.440
 that call the C APIs in TensorFlow.

54:16.440 --> 54:18.360
 And so they're kind of wrappers for that.

54:18.360 --> 54:19.840
 Swift is really kind of special.

54:19.840 --> 54:22.760
 And it's a very different approach.

54:22.760 --> 54:25.360
 Swift for TensorFlow, that is, is a very different approach.

54:25.360 --> 54:26.880
 Because there we're saying, let's

54:26.880 --> 54:28.400
 look at all the problems that need

54:28.400 --> 54:34.080
 to be solved in the full stack of the TensorFlow compilation

54:34.080 --> 54:35.680
 process, if you think about it that way.

54:35.680 --> 54:38.200
 Because TensorFlow is fundamentally a compiler.

54:38.200 --> 54:42.760
 It takes models, and then it makes them go fast on hardware.

54:42.760 --> 54:43.880
 That's what a compiler does.

54:43.880 --> 54:47.560
 And it has a front end, it has an optimizer,

54:47.560 --> 54:49.320
 and it has many back ends.

54:49.320 --> 54:51.680
 And so if you think about it the right way,

54:51.680 --> 54:54.800
 or if you look at it in a particular way,

54:54.800 --> 54:55.560
 it is a compiler.

54:59.280 --> 55:02.120
 And so Swift is merely another front end.

55:02.120 --> 55:05.560
 But it's saying, and the design principle is saying,

55:05.560 --> 55:08.240
 let's look at all the problems that we face as machine

55:08.240 --> 55:11.320
 learning practitioners and what is the best possible way we

55:11.320 --> 55:13.840
 can do that, given the fact that we can change literally

55:13.840 --> 55:15.920
 anything in this entire stack.

55:15.920 --> 55:18.440
 And Python, for example, where the vast majority

55:18.440 --> 55:22.600
 of the engineering and effort has gone into,

55:22.600 --> 55:25.000
 is constrained by being the best possible thing you

55:25.000 --> 55:27.320
 can do with a Python library.

55:27.320 --> 55:29.320
 There are no Python language features

55:29.320 --> 55:31.040
 that are added because of machine learning

55:31.040 --> 55:32.600
 that I'm aware of.

55:32.600 --> 55:34.640
 They added a matrix multiplication operator

55:34.640 --> 55:38.320
 with that, but that's as close as you get.

55:38.320 --> 55:41.460
 And so with Swift, it's hard, but you

55:41.460 --> 55:43.800
 can add language features to the language.

55:43.800 --> 55:46.040
 And there's a community process for that.

55:46.040 --> 55:48.200
 And so we look at these things and say, well,

55:48.200 --> 55:49.720
 what is the right division of labor

55:49.720 --> 55:52.000
 between the human programmer and the compiler?

55:52.000 --> 55:55.280
 And Swift has a number of things that shift that balance.

55:55.280 --> 56:00.560
 So because it has a type system, for example,

56:00.560 --> 56:02.680
 that makes certain things possible for analysis

56:02.680 --> 56:05.560
 of the code, and the compiler can automatically

56:05.560 --> 56:08.880
 build graphs for you without you thinking about them.

56:08.880 --> 56:10.520
 That's a big deal for a programmer.

56:10.520 --> 56:11.680
 You just get free performance.

56:11.680 --> 56:14.400
 You get clustering and fusion and optimization,

56:14.400 --> 56:17.040
 things like that, without you as a programmer

56:17.040 --> 56:20.080
 having to manually do it because the compiler can do it for you.

56:20.080 --> 56:22.240
 Automatic differentiation is another big deal.

56:22.240 --> 56:25.960
 And I think one of the key contributions of the Swift

56:25.960 --> 56:29.640
 TensorFlow project is that there's

56:29.640 --> 56:32.120
 this entire body of work on automatic differentiation

56:32.120 --> 56:34.120
 that dates back to the Fortran days.

56:34.120 --> 56:36.400
 People doing a tremendous amount of numerical computing

56:36.400 --> 56:39.360
 in Fortran used to write these what they call source

56:39.360 --> 56:43.280
 to source translators, where you take a bunch of code,

56:43.280 --> 56:46.640
 shove it into a mini compiler, and it would push out

56:46.640 --> 56:48.080
 more Fortran code.

56:48.080 --> 56:50.240
 But it would generate the backwards passes

56:50.240 --> 56:53.000
 for your functions for you, the derivatives.

56:53.000 --> 56:57.840
 And so in that work in the 70s, a tremendous number

56:57.840 --> 57:01.160
 of optimizations, a tremendous number of techniques

57:01.160 --> 57:02.920
 for fixing numerical instability,

57:02.920 --> 57:05.080
 and other kinds of problems were developed.

57:05.080 --> 57:07.600
 But they're very difficult to port into a world

57:07.600 --> 57:11.280
 where, in eager execution, you get an op by op at a time.

57:11.280 --> 57:13.280
 You need to be able to look at an entire function

57:13.280 --> 57:15.720
 and be able to reason about what's going on.

57:15.720 --> 57:18.720
 And so when you have a language integrated automatic

57:18.720 --> 57:20.520
 differentiation, which is one of the things

57:20.520 --> 57:22.760
 that the Swift project is focusing on,

57:22.760 --> 57:24.680
 you can open all these techniques

57:24.680 --> 57:28.640
 and reuse them in familiar ways.

57:28.640 --> 57:30.120
 But the language integration piece

57:30.120 --> 57:33.240
 has a bunch of design room in it, and it's also complicated.

57:33.240 --> 57:35.680
 The other piece of the puzzle here that's kind of interesting

57:35.680 --> 57:37.560
 is TPUs at Google.

57:37.560 --> 57:40.200
 So we're in a new world with deep learning.

57:40.200 --> 57:42.960
 It constantly is changing, and I imagine,

57:42.960 --> 57:46.360
 without disclosing anything, I imagine

57:46.360 --> 57:48.400
 you're still innovating on the TPU front, too.

57:48.400 --> 57:49.040
 Indeed.

57:49.040 --> 57:53.560
 So how much interplay is there between software and hardware

57:53.560 --> 57:55.240
 in trying to figure out how to together move

57:55.240 --> 57:56.680
 towards an optimized solution?

57:56.680 --> 57:57.760
 There's an incredible amount.

57:57.760 --> 57:59.480
 So we're on our third generation of TPUs,

57:59.480 --> 58:04.640
 which are now 100 petaflops in a very large liquid cooled box,

58:04.640 --> 58:07.720
 virtual box with no cover.

58:07.720 --> 58:11.240
 And as you might imagine, we're not out of ideas yet.

58:11.240 --> 58:14.360
 The great thing about TPUs is that they're

58:14.360 --> 58:17.520
 a perfect example of hardware software co design.

58:17.520 --> 58:19.800
 And so it's about saying, what hardware

58:19.800 --> 58:23.240
 do we build to solve certain classes of machine learning

58:23.240 --> 58:23.840
 problems?

58:23.840 --> 58:26.480
 Well, the algorithms are changing.

58:26.480 --> 58:30.360
 The hardware takes some cases years to produce.

58:30.360 --> 58:32.760
 And so you have to make bets and decide

58:32.760 --> 58:36.520
 what is going to happen and what is the best way to spend

58:36.520 --> 58:39.920
 the transistors to get the maximum performance per watt

58:39.920 --> 58:44.000
 or area per cost or whatever it is that you're optimizing for.

58:44.000 --> 58:46.560
 And so one of the amazing things about TPUs

58:46.560 --> 58:49.960
 is this numeric format called bfloat16.

58:49.960 --> 58:54.120
 bfloat16 is a compressed 16 bit floating point format,

58:54.120 --> 58:55.960
 but it puts the bits in different places.

58:55.960 --> 58:58.960
 And in numeric terms, it has a smaller mantissa

58:58.960 --> 59:00.400
 and a larger exponent.

59:00.400 --> 59:02.960
 That means that it's less precise,

59:02.960 --> 59:05.680
 but it can represent larger ranges of values,

59:05.680 --> 59:07.280
 which in the machine learning context

59:07.280 --> 59:09.960
 is really important and useful because sometimes you

59:09.960 --> 59:13.920
 have very small gradients you want to accumulate

59:13.920 --> 59:17.480
 and very, very small numbers that

59:17.480 --> 59:20.520
 are important to move things as you're learning.

59:20.520 --> 59:23.160
 But sometimes you have very large magnitude numbers as well.

59:23.160 --> 59:26.880
 And bfloat16 is not as precise.

59:26.880 --> 59:28.040
 The mantissa is small.

59:28.040 --> 59:30.360
 But it turns out the machine learning algorithms actually

59:30.360 --> 59:31.520
 want to generalize.

59:31.520 --> 59:34.320
 And so there's theories that this actually

59:34.320 --> 59:36.440
 increases the ability for the network

59:36.440 --> 59:37.960
 to generalize across data sets.

59:37.960 --> 59:41.160
 And regardless of whether it's good or bad,

59:41.160 --> 59:43.680
 it's much cheaper at the hardware level to implement

59:43.680 --> 59:48.080
 because the area and time of a multiplier

59:48.080 --> 59:50.840
 is n squared in the number of bits in the mantissa,

59:50.840 --> 59:53.320
 but it's linear with size of the exponent.

59:53.320 --> 59:55.400
 And you're connected to both efforts

59:55.400 --> 59:57.160
 here both on the hardware and the software side?

59:57.160 --> 59:58.880
 Yeah, and so that was a breakthrough

59:58.880 --> 1:00:01.440
 coming from the research side and people

1:00:01.440 --> 1:00:06.000
 working on optimizing network transport of weights

1:00:06.000 --> 1:00:08.240
 across the network originally and trying

1:00:08.240 --> 1:00:10.160
 to find ways to compress that.

1:00:10.160 --> 1:00:12.120
 But then it got burned into silicon.

1:00:12.120 --> 1:00:14.560
 And it's a key part of what makes TPU performance

1:00:14.560 --> 1:00:17.880
 so amazing and great.

1:00:17.880 --> 1:00:20.680
 Now, TPUs have many different aspects that are important.

1:00:20.680 --> 1:00:25.080
 But the co design between the low level compiler bits

1:00:25.080 --> 1:00:27.360
 and the software bits and the algorithms

1:00:27.360 --> 1:00:28.680
 is all super important.

1:00:28.680 --> 1:00:32.880
 And it's this amazing trifecta that only Google can do.

1:00:32.880 --> 1:00:34.240
 Yeah, that's super exciting.

1:00:34.240 --> 1:00:39.800
 So can you tell me about MLIR project, previously

1:00:39.800 --> 1:00:41.400
 the secretive one?

1:00:41.400 --> 1:00:43.040
 Yeah, so MLIR is a project that we

1:00:43.040 --> 1:00:47.000
 announced at a compiler conference three weeks ago

1:00:47.000 --> 1:00:49.280
 or something at the Compilers for Machine Learning

1:00:49.280 --> 1:00:50.920
 conference.

1:00:50.920 --> 1:00:53.760
 Basically, again, if you look at TensorFlow as a compiler stack,

1:00:53.760 --> 1:00:56.120
 it has a number of compiler algorithms within it.

1:00:56.120 --> 1:00:57.660
 It also has a number of compilers

1:00:57.660 --> 1:00:59.000
 that get embedded into it.

1:00:59.000 --> 1:01:00.480
 And they're made by different vendors.

1:01:00.480 --> 1:01:02.840
 For example, Google has XLA, which

1:01:02.840 --> 1:01:04.680
 is a great compiler system.

1:01:04.680 --> 1:01:06.480
 NVIDIA has TensorRT.

1:01:06.480 --> 1:01:08.640
 Intel has NGRAPH.

1:01:08.640 --> 1:01:10.840
 There's a number of these different compiler systems.

1:01:10.840 --> 1:01:13.840
 And they're very hardware specific.

1:01:13.840 --> 1:01:16.480
 And they're trying to solve different parts of the problems.

1:01:16.480 --> 1:01:19.400
 But they're all kind of similar in a sense of they

1:01:19.400 --> 1:01:20.880
 want to integrate with TensorFlow.

1:01:20.880 --> 1:01:22.960
 Now, TensorFlow has an optimizer.

1:01:22.960 --> 1:01:25.540
 And it has these different code generation technologies

1:01:25.540 --> 1:01:26.440
 built in.

1:01:26.440 --> 1:01:28.720
 The idea of MLIR is to build a common infrastructure

1:01:28.720 --> 1:01:31.160
 to support all these different subsystems.

1:01:31.160 --> 1:01:33.500
 And initially, it's to be able to make it

1:01:33.500 --> 1:01:34.880
 so that they all plug in together

1:01:34.880 --> 1:01:37.880
 and they can share a lot more code and can be reusable.

1:01:37.880 --> 1:01:39.680
 But over time, we hope that the industry

1:01:39.680 --> 1:01:42.480
 will start collaborating and sharing code.

1:01:42.480 --> 1:01:45.320
 And instead of reinventing the same things over and over again,

1:01:45.320 --> 1:01:49.280
 that we can actually foster some of that working together

1:01:49.280 --> 1:01:51.560
 to solve common problem energy that

1:01:51.560 --> 1:01:54.480
 has been useful in the compiler field before.

1:01:54.480 --> 1:01:57.360
 Beyond that, MLIR is some people have joked

1:01:57.360 --> 1:01:59.320
 that it's kind of LLVM too.

1:01:59.320 --> 1:02:01.840
 It learns a lot about what LLVM has been good

1:02:01.840 --> 1:02:04.360
 and what LLVM has done wrong.

1:02:04.360 --> 1:02:06.880
 And it's a chance to fix that.

1:02:06.880 --> 1:02:09.840
 And also, there are challenges in the LLVM ecosystem as well,

1:02:09.840 --> 1:02:12.760
 where LLVM is very good at the thing it was designed to do.

1:02:12.760 --> 1:02:15.560
 But 20 years later, the world has changed.

1:02:15.560 --> 1:02:17.980
 And people are trying to solve higher level problems.

1:02:17.980 --> 1:02:20.360
 And we need some new technology.

1:02:20.360 --> 1:02:24.720
 And what's the future of open source in this context?

1:02:24.720 --> 1:02:25.760
 Very soon.

1:02:25.760 --> 1:02:27.480
 So it is not yet open source.

1:02:27.480 --> 1:02:29.320
 But it will be hopefully in the next couple months.

1:02:29.320 --> 1:02:31.040
 So you still believe in the value of open source

1:02:31.040 --> 1:02:31.640
 in these kinds of contexts?

1:02:31.640 --> 1:02:31.880
 Oh, yeah.

1:02:31.880 --> 1:02:32.440
 Absolutely.

1:02:32.440 --> 1:02:36.160
 And I think that the TensorFlow community at large

1:02:36.160 --> 1:02:37.720
 fully believes in open source.

1:02:37.720 --> 1:02:40.120
 So I mean, there is a difference between Apple,

1:02:40.120 --> 1:02:42.480
 where you were previously, and Google now,

1:02:42.480 --> 1:02:43.520
 in spirit and culture.

1:02:43.520 --> 1:02:45.480
 And I would say the open source in TensorFlow

1:02:45.480 --> 1:02:48.400
 was a seminal moment in the history of software,

1:02:48.400 --> 1:02:51.680
 because here's this large company releasing

1:02:51.680 --> 1:02:56.200
 a very large code base that's open sourcing.

1:02:56.200 --> 1:02:58.520
 What are your thoughts on that?

1:02:58.520 --> 1:03:00.840
 Happy or not, were you to see that kind

1:03:00.840 --> 1:03:02.920
 of degree of open sourcing?

1:03:02.920 --> 1:03:05.360
 So between the two, I prefer the Google approach,

1:03:05.360 --> 1:03:07.800
 if that's what you're saying.

1:03:07.800 --> 1:03:12.400
 The Apple approach makes sense, given the historical context

1:03:12.400 --> 1:03:13.400
 that Apple came from.

1:03:13.400 --> 1:03:15.760
 But that's been 35 years ago.

1:03:15.760 --> 1:03:18.200
 And I think that Apple is definitely adapting.

1:03:18.200 --> 1:03:20.280
 And the way I look at it is that there's

1:03:20.280 --> 1:03:23.160
 different kinds of concerns in the space.

1:03:23.160 --> 1:03:24.880
 It is very rational for a business

1:03:24.880 --> 1:03:28.720
 to care about making money.

1:03:28.720 --> 1:03:31.640
 That fundamentally is what a business is about.

1:03:31.640 --> 1:03:34.880
 But I think it's also incredibly realistic to say,

1:03:34.880 --> 1:03:36.360
 it's not your string library that's

1:03:36.360 --> 1:03:38.080
 the thing that's going to make you money.

1:03:38.080 --> 1:03:41.480
 It's going to be the amazing UI product differentiating

1:03:41.480 --> 1:03:43.840
 features and other things like that that you built on top

1:03:43.840 --> 1:03:45.280
 of your string library.

1:03:45.280 --> 1:03:48.280
 And so keeping your string library

1:03:48.280 --> 1:03:50.360
 proprietary and secret and things

1:03:50.360 --> 1:03:54.760
 like that is maybe not the important thing anymore.

1:03:54.760 --> 1:03:57.720
 Where before, platforms were different.

1:03:57.720 --> 1:04:01.520
 And even 15 years ago, things were a little bit different.

1:04:01.520 --> 1:04:02.920
 But the world is changing.

1:04:02.920 --> 1:04:04.840
 So Google strikes a very good balance,

1:04:04.840 --> 1:04:05.340
 I think.

1:04:05.340 --> 1:04:09.040
 And I think that TensorFlow being open source really

1:04:09.040 --> 1:04:12.000
 changed the entire machine learning field

1:04:12.000 --> 1:04:14.080
 and caused a revolution in its own right.

1:04:14.080 --> 1:04:17.560
 And so I think it's amazingly forward looking

1:04:17.560 --> 1:04:20.880
 because I could have imagined, and I wasn't at Google

1:04:20.880 --> 1:04:23.160
 at the time, but I could imagine a different context

1:04:23.160 --> 1:04:25.520
 and different world where a company says,

1:04:25.520 --> 1:04:27.640
 machine learning is critical to what we're doing.

1:04:27.640 --> 1:04:29.640
 We're not going to give it to other people.

1:04:29.640 --> 1:04:35.560
 And so that decision is a profoundly brilliant insight

1:04:35.560 --> 1:04:37.480
 that I think has really led to the world being

1:04:37.480 --> 1:04:40.120
 better and better for Google as well.

1:04:40.120 --> 1:04:42.200
 And has all kinds of ripple effects.

1:04:42.200 --> 1:04:45.160
 I think it is really, I mean, you

1:04:45.160 --> 1:04:48.800
 can't understate Google deciding how profound that

1:04:48.800 --> 1:04:49.840
 is for software.

1:04:49.840 --> 1:04:50.880
 It's awesome.

1:04:50.880 --> 1:04:54.900
 Well, and again, I can understand the concern

1:04:54.900 --> 1:04:58.440
 about if we release our machine learning software,

1:04:58.440 --> 1:05:00.000
 our competitors could go faster.

1:05:00.000 --> 1:05:02.500
 But on the other hand, I think that open sourcing TensorFlow

1:05:02.500 --> 1:05:03.960
 has been fantastic for Google.

1:05:03.960 --> 1:05:09.120
 And I'm sure that decision was very nonobvious at the time,

1:05:09.120 --> 1:05:11.480
 but I think it's worked out very well.

1:05:11.480 --> 1:05:13.240
 So let's try this real quick.

1:05:13.240 --> 1:05:15.640
 You were at Tesla for five months

1:05:15.640 --> 1:05:17.640
 as the VP of autopilot software.

1:05:17.640 --> 1:05:20.520
 You led the team during the transition from H hardware

1:05:20.520 --> 1:05:22.360
 one to hardware two.

1:05:22.360 --> 1:05:23.520
 I have a couple of questions.

1:05:23.520 --> 1:05:26.320
 So one, first of all, to me, that's

1:05:26.320 --> 1:05:33.000
 one of the bravest engineering decisions undertaking really

1:05:33.000 --> 1:05:36.040
 ever in the automotive industry to me, software wise,

1:05:36.040 --> 1:05:37.440
 starting from scratch.

1:05:37.440 --> 1:05:39.200
 It's a really brave engineering decision.

1:05:39.200 --> 1:05:42.600
 So my one question there is, what was that like?

1:05:42.600 --> 1:05:43.920
 What was the challenge of that?

1:05:43.920 --> 1:05:45.720
 Do you mean the career decision of jumping

1:05:45.720 --> 1:05:48.800
 from a comfortable good job into the unknown, or?

1:05:48.800 --> 1:05:51.480
 That combined, so at the individual level,

1:05:51.480 --> 1:05:54.560
 you making that decision.

1:05:54.560 --> 1:05:57.960
 And then when you show up, it's a really hard engineering

1:05:57.960 --> 1:05:58.760
 problem.

1:05:58.760 --> 1:06:03.560
 So you could just stay, maybe slow down,

1:06:03.560 --> 1:06:06.680
 say hardware one, or those kinds of decisions.

1:06:06.680 --> 1:06:10.160
 Just taking it full on, let's do this from scratch.

1:06:10.160 --> 1:06:11.080
 What was that like?

1:06:11.080 --> 1:06:12.640
 Well, so I mean, I don't think Tesla

1:06:12.640 --> 1:06:16.080
 has a culture of taking things slow and seeing how it goes.

1:06:16.080 --> 1:06:18.080
 And one of the things that attracted me about Tesla

1:06:18.080 --> 1:06:20.020
 is it's very much a gung ho, let's change the world,

1:06:20.020 --> 1:06:21.520
 let's figure it out kind of a place.

1:06:21.520 --> 1:06:25.640
 And so I have a huge amount of respect for that.

1:06:25.640 --> 1:06:28.680
 Tesla has done very smart things with hardware one

1:06:28.680 --> 1:06:29.400
 in particular.

1:06:29.400 --> 1:06:32.200
 And the hardware one design was originally

1:06:32.200 --> 1:06:36.560
 designed to be very simple automation features

1:06:36.560 --> 1:06:39.360
 in the car for like traffic aware cruise control and things

1:06:39.360 --> 1:06:39.840
 like that.

1:06:39.840 --> 1:06:42.920
 And the fact that they were able to effectively feature creep

1:06:42.920 --> 1:06:47.720
 it into lane holding and a very useful driver assistance

1:06:47.720 --> 1:06:50.120
 feature is pretty astounding, particularly given

1:06:50.120 --> 1:06:52.560
 the details of the hardware.

1:06:52.560 --> 1:06:54.640
 Hardware two built on that in a lot of ways.

1:06:54.640 --> 1:06:56.180
 And the challenge there was that they

1:06:56.180 --> 1:07:00.040
 were transitioning from a third party provided vision stack

1:07:00.040 --> 1:07:01.720
 to an in house built vision stack.

1:07:01.720 --> 1:07:05.680
 And so for the first step, which I mostly helped with,

1:07:05.680 --> 1:07:08.480
 was getting onto that new vision stack.

1:07:08.480 --> 1:07:10.800
 And that was very challenging.

1:07:10.800 --> 1:07:14.000
 And it was time critical for various reasons,

1:07:14.000 --> 1:07:14.960
 and it was a big leap.

1:07:14.960 --> 1:07:16.640
 But it was fortunate that it built

1:07:16.640 --> 1:07:18.800
 on a lot of the knowledge and expertise and the team

1:07:18.800 --> 1:07:22.920
 that had built hardware one's driver assistance features.

1:07:22.920 --> 1:07:25.360
 So you spoke in a collected and kind way

1:07:25.360 --> 1:07:28.960
 about your time at Tesla, but it was ultimately not a good fit.

1:07:28.960 --> 1:07:31.840
 Elon Musk, we've talked on this podcast,

1:07:31.840 --> 1:07:33.880
 several guests to the course, Elon Musk

1:07:33.880 --> 1:07:36.880
 continues to do some of the most bold and innovative engineering

1:07:36.880 --> 1:07:39.560
 work in the world, at times at the cost

1:07:39.560 --> 1:07:41.280
 some of the members of the Tesla team.

1:07:41.280 --> 1:07:45.080
 What did you learn about working in this chaotic world

1:07:45.080 --> 1:07:46.720
 with Elon?

1:07:46.720 --> 1:07:50.560
 Yeah, so I guess I would say that when I was at Tesla,

1:07:50.560 --> 1:07:54.440
 I experienced and saw the highest degree of turnover

1:07:54.440 --> 1:07:58.240
 I'd ever seen in a company, which was a bit of a shock.

1:07:58.240 --> 1:08:00.520
 But one of the things I learned and I came to respect

1:08:00.520 --> 1:08:03.760
 is that Elon's able to attract amazing talent because he

1:08:03.760 --> 1:08:05.660
 has a very clear vision of the future,

1:08:05.660 --> 1:08:07.200
 and he can get people to buy into it

1:08:07.200 --> 1:08:09.840
 because they want that future to happen.

1:08:09.840 --> 1:08:11.840
 And the power of vision is something

1:08:11.840 --> 1:08:14.240
 that I have a tremendous amount of respect for.

1:08:14.240 --> 1:08:17.040
 And I think that Elon is fairly singular

1:08:17.040 --> 1:08:20.120
 in the world in terms of the things

1:08:20.120 --> 1:08:22.360
 he's able to get people to believe in.

1:08:22.360 --> 1:08:27.360
 And there are many people that stand in the street corner

1:08:27.360 --> 1:08:30.200
 and say, ah, we're going to go to Mars, right?

1:08:30.200 --> 1:08:31.600
 But then there are a few people that

1:08:31.600 --> 1:08:35.200
 can get others to buy into it and believe and build the path

1:08:35.200 --> 1:08:36.160
 and make it happen.

1:08:36.160 --> 1:08:39.120
 And so I respect that.

1:08:39.120 --> 1:08:41.880
 I don't respect all of his methods,

1:08:41.880 --> 1:08:45.000
 but I have a huge amount of respect for that.

1:08:45.000 --> 1:08:46.920
 You've mentioned in a few places,

1:08:46.920 --> 1:08:50.440
 including in this context, working hard.

1:08:50.440 --> 1:08:52.000
 What does it mean to work hard?

1:08:52.000 --> 1:08:53.520
 And when you look back at your life,

1:08:53.520 --> 1:08:57.080
 what were some of the most brutal periods

1:08:57.080 --> 1:09:00.760
 of having to really put everything

1:09:00.760 --> 1:09:03.360
 you have into something?

1:09:03.360 --> 1:09:05.040
 Yeah, good question.

1:09:05.040 --> 1:09:07.440
 So working hard can be defined a lot of different ways,

1:09:07.440 --> 1:09:12.480
 so a lot of hours, and so that is true.

1:09:12.480 --> 1:09:14.520
 The thing to me that's the hardest

1:09:14.520 --> 1:09:18.760
 is both being short term focused on delivering and executing

1:09:18.760 --> 1:09:21.120
 and making a thing happen while also thinking

1:09:21.120 --> 1:09:24.400
 about the longer term and trying to balance that.

1:09:24.400 --> 1:09:28.520
 Because if you are myopically focused on solving a task

1:09:28.520 --> 1:09:31.240
 and getting that done and only think

1:09:31.240 --> 1:09:32.600
 about that incremental next step,

1:09:32.600 --> 1:09:36.440
 you will miss the next big hill you should jump over to.

1:09:36.440 --> 1:09:39.600
 And so I've been really fortunate that I've

1:09:39.600 --> 1:09:42.120
 been able to kind of oscillate between the two.

1:09:42.120 --> 1:09:45.480
 And historically at Apple, for example, that

1:09:45.480 --> 1:09:47.920
 was made possible because I was able to work with some really

1:09:47.920 --> 1:09:50.360
 amazing people and build up teams and leadership

1:09:50.360 --> 1:09:55.280
 structures and allow them to grow in their careers

1:09:55.280 --> 1:09:58.280
 and take on responsibility, thereby freeing up

1:09:58.280 --> 1:10:02.960
 me to be a little bit crazy and thinking about the next thing.

1:10:02.960 --> 1:10:04.640
 And so it's a lot of that.

1:10:04.640 --> 1:10:06.760
 But it's also about with experience,

1:10:06.760 --> 1:10:10.080
 you make connections that other people don't necessarily make.

1:10:10.080 --> 1:10:12.880
 And so I think that's a big part as well.

1:10:12.880 --> 1:10:16.000
 But the bedrock is just a lot of hours.

1:10:16.000 --> 1:10:19.600
 And that's OK with me.

1:10:19.600 --> 1:10:21.480
 There's different theories on work life balance.

1:10:21.480 --> 1:10:25.200
 And my theory for myself, which I do not project onto the team,

1:10:25.200 --> 1:10:28.520
 but my theory for myself is that I

1:10:28.520 --> 1:10:30.400
 want to love what I'm doing and work really hard.

1:10:30.400 --> 1:10:35.000
 And my purpose, I feel like, and my goal is to change the world

1:10:35.000 --> 1:10:36.280
 and make it a better place.

1:10:36.280 --> 1:10:40.000
 And that's what I'm really motivated to do.

1:10:40.000 --> 1:10:44.760
 So last question, LLVM logo is a dragon.

1:10:44.760 --> 1:10:47.880
 You explain that this is because dragons have connotations

1:10:47.880 --> 1:10:50.320
 of power, speed, intelligence.

1:10:50.320 --> 1:10:53.320
 It can also be sleek, elegant, and modular,

1:10:53.320 --> 1:10:56.280
 though you remove the modular part.

1:10:56.280 --> 1:10:58.920
 What is your favorite dragon related character

1:10:58.920 --> 1:11:01.440
 from fiction, video, or movies?

1:11:01.440 --> 1:11:03.840
 So those are all very kind ways of explaining it.

1:11:03.840 --> 1:11:06.200
 Do you want to know the real reason it's a dragon?

1:11:06.200 --> 1:11:07.000
 Yeah.

1:11:07.000 --> 1:11:07.920
 Is that better?

1:11:07.920 --> 1:11:11.040
 So there is a seminal book on compiler design

1:11:11.040 --> 1:11:12.520
 called The Dragon Book.

1:11:12.520 --> 1:11:16.320
 And so this is a really old now book on compilers.

1:11:16.320 --> 1:11:22.080
 And so the dragon logo for LLVM came about because at Apple,

1:11:22.080 --> 1:11:24.720
 we kept talking about LLVM related technologies

1:11:24.720 --> 1:11:26.960
 and there's no logo to put on a slide.

1:11:26.960 --> 1:11:28.480
 And so we're like, what do we do?

1:11:28.480 --> 1:11:30.480
 And somebody's like, well, what kind of logo

1:11:30.480 --> 1:11:32.160
 should a compiler technology have?

1:11:32.160 --> 1:11:33.360
 And I'm like, I don't know.

1:11:33.360 --> 1:11:37.320
 I mean, the dragon is the best thing that we've got.

1:11:37.320 --> 1:11:41.520
 And Apple somehow magically came up with the logo.

1:11:41.520 --> 1:11:42.680
 And it was a great thing.

1:11:42.680 --> 1:11:44.520
 And the whole community rallied around it.

1:11:44.520 --> 1:11:46.760
 And then it got better as other graphic designers

1:11:46.760 --> 1:11:47.360
 got involved.

1:11:47.360 --> 1:11:49.360
 But that's originally where it came from.

1:11:49.360 --> 1:11:50.160
 The story.

1:11:50.160 --> 1:11:51.960
 Is there dragons from fiction that you

1:11:51.960 --> 1:11:57.240
 connect with, that Game of Thrones, Lord of the Rings,

1:11:57.240 --> 1:11:58.080
 that kind of thing?

1:11:58.080 --> 1:11:59.200
 Lord of the Rings is great.

1:11:59.200 --> 1:12:00.760
 I also like role playing games and things

1:12:00.760 --> 1:12:02.240
 like computer role playing games.

1:12:02.240 --> 1:12:04.280
 And so dragons often show up in there.

1:12:04.280 --> 1:12:07.160
 But really, it comes back to the book.

1:12:07.160 --> 1:12:09.960
 Oh, no, we need a thing.

1:12:09.960 --> 1:12:13.720
 And hilariously, one of the funny things about LLVM

1:12:13.720 --> 1:12:19.520
 is that my wife, who's amazing, runs the LLVM Foundation.

1:12:19.520 --> 1:12:21.080
 And she goes to Grace Hopper and is

1:12:21.080 --> 1:12:23.360
 trying to get more women involved in the.

1:12:23.360 --> 1:12:24.640
 She's also a compiler engineer.

1:12:24.640 --> 1:12:26.080
 So she's trying to get other women

1:12:26.080 --> 1:12:28.020
 to get interested in compilers and things like this.

1:12:28.020 --> 1:12:30.000
 And so she hands out the stickers.

1:12:30.000 --> 1:12:34.320
 And people like the LLVM sticker because of Game of Thrones.

1:12:34.320 --> 1:12:36.880
 And so sometimes culture has this helpful effect

1:12:36.880 --> 1:12:39.960
 to get the next generation of compiler engineers

1:12:39.960 --> 1:12:42.400
 engaged with the cause.

1:12:42.400 --> 1:12:43.320
 OK, awesome.

1:12:43.320 --> 1:12:44.800
 Chris, thanks so much for talking with us.

1:12:44.800 --> 1:13:05.920
 It's been great talking with you.