How to pronounce“cuda”in English

Cuda kernel for your GPU it feels like you're writing a scaler program so you're like you have ifs you have for Loops stuff like this you're just

Cudahy, Paramount, Gardena, Commerce, Bell Gardens, and Bell-- places that were cattle ranches and farmland before they morphed into lower-middle class

okay Cuda is a different thing with a different set of tools a different world a different way of thinking and we don't have one world that scales and I think

Richard Cudalary Jr.

After we launched CUDA, I recognized that it was going to add so much cost, but it was something we believed in.

It predated CUDA by about three years.

how do we attract developers to CUDA?

And I'll say something about CUDA at the, at the end.

We've been doing things with CUDA now for-for two or three years as we're programming on

And they didn't understand CUDA, but they understood their deep learning.

And then maybe C++ calls into some CUDA kernels, which are Turing complete. The CUDA kernels go through LVM, which is Turing complete,

These things basically were CPUs and CUDA.

so the the the they call them cuda ninjas i love it to get to the optimal point you either write a pre

and nvidia invested for years in cuda first for hpc and then they got lucky with the ai trend but do you think they're going to

Well, the problem was CUDA increased our cost of that GPU, which is a consumer product, so tremendously, it completely consumed all of the company's gross profit dollars.

benefits of, of CUDA is that it's, you know, on the one hand, an incredible accelerator.

I don't know how many people here write CUDA code, or GPU code?

The other is sort of the fine grain OpenMP NVIDIA CUDA type approach.

And we hired someone really good at writing CUDA kernels, it keeps on becoming an incremental additive as well as multiplicative force.

we went to universities and wrote books and taught classes and put CUDA everywhere.

So you could imagine a world where GeForce would carry the burden of CUDA and none of the gamers would appreciate it and none of the gamers would pay for it.

wanted to know was there any precursor to the impact was the impact just a Cuda

separate out syntax becomes what is that programming model and so do you want the Cuda style I write one program and it runs many places the um do you want the

Well, putting CUDA on GeForce, that was a strategic decision that was very, very hard to do, because it cost the

So, putting CUDA on GeForce, could not afford to do?

So, CUDA turned out to be

clawed our way way back slowly, but we carried CUDA on GeForce. I always say that NVIDIA is the house that GeForce built, because it was GeForce that took CUDA out to

So, as I understand, that went below CUDA, so they go super low programming of GPUs.

It's not exactly that, but that's still part of technically CUDA.

But on the other hand, they're spending a lot of time writing CUDA curls, right?

commercial version of number called number pro and what number pro did is it targeted gpus so we had the very first cuda jit and the very first

pushing we're pushing people to write software in different ways and so we're pushing people to write Cuda so they can get GPU compute and the the thousands of

CPUs and so if you want to talk to a GPU now you're talking to its cousin Cuda

uh program towards makes it so that by default you get parallel systems if you look at Cuda Cuda is a point halfway in the space where in Cuda when you write a

The Cg path took us to eventually CUDA. CUDA, step by step by step

We were already selling millions and millions of GeForce GPUs a year, and we said, "You know, we ought to put CUDA on GeForce

Researchers, scientists, they discovered CUDA on GeForce because they were all, you know...

That's the reason why CUDA has been so resilient on the one hand, and yet we continue to enhance it. We're at CUDA 13.2, and so we're

For example, to get highly efficient training, they're making modifications at or below the CUDA layer for Nvidia chips.

prestigious School in the world Stanford you have a question that you want answered without analytical ability just facts you push Google and the Cuda

in the literature, versus then going in and writing all the codes, maybe the CUDA kernels, maybe whatever else.

And so when you say that, you say, go do this big operation, a matrix multiplication, and if it's on a GPU, kick off a CUDA kernel.

And so you say, wait a second, how much faster could all this progress go if they were not having to hand write all these CUDA curls, right?

actually they all have to use that if you want to use gpu acceleration they have to all use the same underlying drivers and same gpu cuda things so it's

So, we invented this thing called CUDA, and