[gymbeaux]

As “humble” as NVIDIA’s CEO appears to be, NVIDIA the company (he’s been running this whole time), made decision after decision with the simple intention of killing off its competition (ATI/AMD). Gameworks is my favorite example- essentially if you wanted a video game to look as good as possible, you needed an NVIDIA GPU. Those same games played on AMD GPUs just didn’t look as good.

Now that video gaming is secondary (tertiary?) to Nvidia’s revenue stream, they could give a shit which brand gamers prefer. It’s small time now. All that matters is who companies are buying their GPUs from for AI stuff. Break down that CUDA wall and it’s open-season. I wonder how they plan to stave that off. It’s only a matter of time before people get tired of writing C++ code to interface with CUDA.

[mike_hearn]

You don't need to use C++ to interface with CUDA or even write it.

A while ago NVIDIA and the GraalVM team demoed grCUDA which makes it easy to share memory with CUDA kernels and invoke them from any managed language that runs on GraalVM (which includes JIT compiled Python). Because it's integrated with the compiler the invocation overhead is low:

https://developer.nvidia.com/blog/grcuda-a-polyglot-language...

And TornadoVM lets you write kernels in JVM langs that are compiled through to CUDA:

https://www.tornadovm.org

There are similar technologies for other languages/runtimes too. So I don't think that will cause NVIDIA to lose ground.

[gymbeaux]

So these alternatives exist yes, but are they “production ready”- in other words, are they being used. My opinion is that while you can use another language, most companies for one reason or another are still using C++. I just don’t really know what the reason(s) are.

I think about other areas in tech where you can use whatever language, but it isn’t practical to do so. I can write a backend API server in Swift… or perhaps more relevant- I can use AMD’s ROCm to do… anything.