Chris Lattner: recommendation

Transcript context

…I'll show a decomposition of performance. Okay, well, yeah. So you can talk about that too. So one of the really funny things that if you get it wrong, it's very difficult to fix is asynchrony. And so when you think about, I have a CPU and I have a GPU and they talk to each other, most people think about it in terms of CPUs doing some stuff that throws a CUDA kernel across the fence, GPUs go brr, right? And then when there's results, you know, you read it back, right? But that's actually a really inefficient way to run a computer. What you actually want is you want to think about there's two different computers that are both executing and they're sending messages back and forth to each other. So I built hardware, right? If you go all the way down to the gates, when you look at this, these computers, whether they're the tiled cerebrus wafer thing, right? Hardware is implicitly parallel. All of these things are always running all the time and they're communicating with each other. And so starting from an asynchronous programming model means that you can get accelerators that send messages to each other because that's the natural form of the hardware. When you get into CPUs, CPUs, you have, you know, 88 core CPUs or a hundred core CPUs these days, even if you have four, right? What they really are is there are four completely independent computers. And so, yeah, they send cash lines across the fabric at each other, right? But they're async, right? And so much of the programming model that people start with is always sync. And so when you build into the stuff, you say, okay, well, that's a huge problem. The consequence of getting this right is that now you get overlapping work and it comes for free, right? And again, simplicity, the right architecture leads to the thing just magically happening. One of the great projects we did at Google back in the day involved some of this stuff and it led to a 2x improvement in ads throughput. Ads is a very tuned workload, right? And getting TPUs and CPUs to work at the same time and overlap that compute was a huge deal. And the fact that it just falls out of an async architecture is quite important. And again, you look at this at all levels of granularity, networking is asynchronous. So as soon as you distribute a compute problem across a network, async is there, right? And so all of these systems are kind of designed in the wrong way. You go up a level of the stack. So you have these operators, right? Super interesting how this whole ecosystem evolved. If you dig into something like TensorFlow or PyTorch, right? You know, you get to the point where you have a matrix multiplication. And so like you've talked about before on your podcast, kernel fusion is really important. And the way people did that historically is they say, okay, well, I have a matrix multiplication and oh gosh, it's often followed by a ReLU. Well, I'll make a MatMul ReLU fused kernel, right? Cool, and that's a huge performance improvement because ReLU is just a max operation and you avoid tons of memory traffic, all good stuff, right? LU. Well, I'll make a MatMul ReLU fused kernel, right? Cool, and that's a huge performance improvement because ReLU is just a max operation and you avoid tons of memory traffic, all good stuff, right? You run into these scalability problems because now you get things like a fused attention layer. So what is the consequence of saying, I'm going to manually tune the things that are important for mlperf or something, right? Well, what ends up happening is, again, you get these happy paths and they work way better than the default path. And so if you look within the NVIDIA world, for example, there's a ton of focus on transformers. And so NVIDIA goes and they build this really cool library called Faster Transformer. The performance point of using it is massive. Like it's a big deal. And so a lot of LLM companies and other folks use this thing because they want the performance. Performance turns into cost and throughput and all good things. Here's the problem. If you want to go innovate in transformers, now you're constrained by what Faster Transformer can do, right? And so, again, you come back to where are compilers useful. They're useful for generalization. And so if you can get the same quality result or better than Faster Transformer, but with a generalized architecture, well, now you can get the best of both worlds where you have orthogonality and composability, you enable research, you also get better performance. One of the things that you ask, like, how can we beat state of the art? Well, it's because it turns out compilers have more attention span. And it turns out that what's happened, even within like the NVIDIA product line, or even within the Intel product line, or even within one vendor's line of technologies, is that they have to build these little compilers because there's so much variation across the product family. If you look at an Intel product family, for example, they're building software that has to run on many different versions of this architecture. And they come out and they add a cool new dot product instruction, or they add beeflet 16 support, or they add whatever. And so what's been happening in the industry is that each of these companies have been building their own little compilers. And so their own little compilers are, again, they're focused on one part of the PROM domain. They have all these issues. They're not scaled very well. And so you get either, again, another fragmented part of the space where something will work really well, usually for a benchmark, right? But then it doesn't work well when people try to do new things. And so kernel fusion turns out to be one of those things. The programmability side, right? I mean, you just keep working your way up the stack. Matrix multiplication is really important. So who's that thing that hasn't been invented yet? I mean, we have folks that are using our stuff that care about computational fluid dynamics, right? And things like this, where it's really more of HPC, linear algebra, like more general than deep learning, right? And they want to use the same technology because all this technology is general purpose.…

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