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Comment by ur-whale | Hacker News Reader

As is usual for NVidia: great hardware, an effing nightmare figuring out how to setup the pile of crap they call software.

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If you think their software is bad try using any other vendor , makes nvidia looks amazing. Apple is only one close

Pretty much this. Nvidia isn't big because of their hardware, they're not ahead on that front. It's their software support that makes it worthwhile.

Although a bit off the GPU topic, I think Apple's Rosetta is the smoothest binary transition I've ever used.

Keep in mind this is part of Nvidias embedded offerings. So you will get one release of software ever, and that's gonna be pretty much it for the lifetime of the product.

Fascinating to me managing some of these systems just how bad the software is.

Management becomes layers upon layers of bash scripts which ends up calling a final batch script written by Mellanox.

They'll catch up soon, but you end up having to stay strictly on their release cycle always.

Lots of effort.

And yet CUDA has looked way better than ATi/AMD offerings in the same area despite ATi/AMD technically being first to deliver GPGPU (major difference is that CUDA arrived year later but supported everything from G80 up, and nicely evolved, while AMD managed to have multiple platforms with patchy support and total rewrites in between)

What was the AMD GPGPU called?

Which one? We first had the flurry of third party work (Brook, Lib Sh, etc), then we had AMD "Close to Metal" which was IIRC based on Brook, soon followed with dedicated cards, year later we got CUDA (also derived partially from Brook!) and AMD Stream SDK, later renamed APP SDK. Then we got HIP / HSA stuff which unfortunately has its biggest legacy (outside of availability of HIP as way to target ROCm and CUDA simultaneously) in low level details of how GPU game programming evolved on Xbox360 / PS4 / XBox One / PS5. Somewhere in between AMD seemed to bet on OpenCL, yet today with latest drivers from both AMD and nVidia I get more OpenCL features on nVidia.

And of course there's the part of totally random and inconsistent support outside of the few dedicated cards, which is honestly why CUDA the de facto standard everyone measures against - you could run CUDA applications, if slowly, even on the lowest end nvidia cards, like Quadro NVS series (think lowest end GeForce chip but often paired with more displays and different support that focused on business users that didn't need fast 3D). And you still can, generally, run core CUDA code within last few generations on everything from smallest mobile chip to biggest datacenter behemoth.

You forgot the C++AMP collaboration with Microsoft.

Is it the OpenMP related one or another thing?

I kinda lost track, this whole thread reminded me how hopeful I was to play with GPGPU with my then new X1600

Other thing,

https://learn.microsoft.com/en-us/cpp/parallel/amp/cpp-amp-c...

Try to use Intel or AMD stuff instead.

Except the performance people are seeing is way below expectations. It seems to be slower than an M4. Which kind of defeats the purpose. It was advertised as 1 Petaflop on your desk.

But maybe this will change? Software issues somehow?

It also runs CUDA, which is useful

it fits bigger models and you can stack them.

plus apparently some of the early benchmarks were made with ollama and should be disregarded

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