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Comment by BobbyJo | Hacker News Reader

This. I worked in HPC between 2010 and 2013, and people were trying to compete with NVidia in the GPGPU space even back then, seeing the way the wind was blowing. Compute bandwidth, and FLOPS/watt has steadily been more and more important the last 15 years, even before AI.

NVidia has continued to stay ahead because every alternative to CUDA is half baked trash, even when the silicone make sense. As a tech company, trading $$ for time not spent dealing with compatibility bugs and broken drivers pretty much always makes business sense.

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Nvidia did a lot of things right. But it sure didn't hurt that the crypto bubble gave way to LLMs with almost perfect timing. Luck favors the prepared and all that but Nvidia also would have had trouble timing market changes better.

Yeah, exactly, it has never been clear that we would find a use for parallel computation outside of niches like graphics and HPC. And in fact most of the history of computing has been evidence of the opposite: useful computation has been predominantly serial in nature. But here we are now.

Nvidia did ride a perfect storm of gaming + bitcoin + AI.

But AMD and others could have done the same, had they been better at riding that wave. There’s a reason it was Nvidia who won out.

> half baked trash

> dealing with compatibility bugs

> broken drivers

Describes my experience trying to use CUDA perfectly.

We have a long way to go and we haven't even started yet.

Given that experience, think about what state the alternatives must be in!

To be fair, Cuda has improved a lot since 2014 or so. I messed up my Linux box multiple times trying to install Cuda but the last time it was just apt install and maybe setting ld library and it all just worked.

It’s pretty bad. just when you think you can order AMD chips since there is no shortage, and use a translation layer and have a cheap AI datacenter, it turns out AMD is fumbling the ball at every step of the way

It’s interesting. They have had plenty of time and resources available to mount solid competition. Why haven’t they? Is it a talent hiring problems or some more fundamental problem with their engineering processes? The writing has been on the wall for gpgpu for more than 10 years. Definitely enough time to catch up.

Its a commitment problem IMO. NVidia stuck with CUDA for a long time before it started paying them what it cost. AMD and Intel have both launched and killed initiatives a couple times each, but abandon them within a few years because adoption didn't happen overnight.

If you need people to abandon an ecosystem thats been developed steadily over nearly 20 years for your shiny new thing in order to keep it around, you'll never compete.

Have you tried Vulkan Compute or OpenCL? I recommend evaluating this relatively, vice compared to an ideal.

Wasn't Intel's commercial compiler also dominated HPC space around that time period? I remember back then people would use cuda and icc for hpc stuff.

I believe their compilers still dominate in CPU-bound workloads.

I can't speak to modern hpc, since I've been out of the game for a few years but icc was absolutely the preferred compiler for almost any workload and Intel procs were the desired hardware to run on. AMD was joked about as the only reason Intel wasn't considered a monopoly at the time and that Intel would happily take wheelbarrows of cash over to AMD to prop them up, just to ensure they didn't even appear like a monopoly. But no one in the field was buying AMD procs for their workloads.

Most super computers were still Intel/IBM, but around that time is when the shift to GPU clusters started. #1 super computer spot was taken by an Nvidia cluster in 2012, but I remember other big projects were done before as well.

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