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AMD may get across the CUDA moat

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Re: AMD may get across the CUDA moat

#61

Post by SoftTalker »

treprinum wrote: post #54
Zambyte wrote: post #46

Earlier quoted context omitted.

Is it better than AMD? I have had literally no graphics issues on my 6650 XT with swaywm using the built in kernel drivers.

I never had an issue with nVidia drivers on Linux in the past 5 years, but recently bought a laptop with a 4090 and AMD CPU. Now I get random freezes, often right after I login into Cinnamon but can't really tell if it's the nVidia driver for 4090, AMDGPU driver for integrated RDNA, kernel 6.2 or Cinnamon issue. The laptop just hangs and stops responding to keyboard so I can't login to console and dmesg it.

The main issue with Nvidia on Linux AIUI is that they don't release the source code for their drivers.

Re: AMD may get across the CUDA moat

#62

Post by frnkng »

As a former ETH miner I learned the hard way that saving a few bucks on hardware may not be worth operational issues.

I had a miner running with Nividia cards and a miner running with AMD cards. One of them had massive maintenance demand and the other did not. I will not state which brand was better imho.

Currently I estimate that running miners and running gpu servers has similar operational requirements and finally at scale similar financial considerations.

So, whatever is cheapest to operate in terms of time expenditure, hw cost, energy use,… will be used the most.

P.s.: I ran the mining operation not to earn money but mainly out of curiosity. And it was a small scale business powered by a pv system and a attached heat pump.

Re: AMD may get across the CUDA moat

#63

Post by PH95VuimJjqBqy »

javchz wrote: post #26

CUDA is the only reason I have an Nvidia card, but if more projects start migrating to a more agnostic environment, I'll be really grateful. Running Nvidia in Linux isn't as much fun. Fedora and Debian can be incredibly reliable systems, but when you add an Nvidia card, I feel like I am back in Windows Vista with kernel crashes from time to time.

I see these complains from time to time and I never understand them.

I've literally been running nvidia on linux since the TNT2 days and have _never_ had this sort of issue. That's across many drivers and many cards over the many many years.

Re: AMD may get across the CUDA moat

#64

Post by ant6n »

PH95VuimJjqBqy wrote: post #63
javchz wrote: post #26

CUDA is the only reason I have an Nvidia card, but if more projects start migrating to a more agnostic environment, I'll be really grateful. Running Nvidia in Linux isn't as much fun. Fedora and Debian can be incredibly reliable systems, but when you add an Nvidia card, I feel like I am back in Windows Vista with kernel crashes from time to time.

I see these complains from time to time and I never understand them. I've literally been running nvidia on linux since the TNT2 days and have _never_ had this sort of issue. That's across many drivers and many cards over the many many years.

Well tnt2 should be pretty well supported by now ;-)

Re: AMD may get across the CUDA moat

#66

Post by latchkey »

frnkng wrote: post #62

As a former ETH miner I learned the hard way that saving a few bucks on hardware may not be worth operational issues. I had a miner running with Nividia cards and a miner running with AMD cards. One of them had massive maintenance demand and the other did not. I will not state which brand was better imho. Currently I estimate that running miners and running gpu servers has similar operational requirements and finally…

I ran 150,000+ AMD cards for mining ETH. Once I fully automated all the vbios installs and individual card tuning, it ran beautifully. Took a lot of work to get there though!

Fact is that every single GPU chip is a snowflake. No two operate the same.

Re: AMD may get across the CUDA moat

#67

Post by mikepurvis »

omneity wrote: post #52

I was able to use ROCm recently with Pytorch and after pulling some hair it worked quite well. The Radeon GPU I had on hand was a bit old and underpowered (RDNA2) and it only supported matmul on fp64, but for the job I needed done I saw a 200x increase in it/s over CPU despite the need to cast everywhere, and that made me super happy. Best of all is that I simply set the device to `torch.device('cuda')` rather than o…

Sigh. It's great that these container images exist to give people an easy on-ramp, but they definitely don't work for every use case (especially once you're in embedded where space matters and you might not be online to pull multi-gb updates from some registry).

So it's important that vendors don't feel let off the hook to provide sane packaging just because there's an option to use a kitchen-sink container image they rebuild every day from source.

Re: AMD may get across the CUDA moat

#68

Post by latchkey »

nabla9 wrote: post #28

> Crossing the CUDA moat for AMD GPUs may be as easy as using PyTorch. Nvidia has spent huge amount of work to make code run smoothly and fast. AMD has to work hard to catch up. ROCm code is slower , has more bugs, don't have enough features and they have compatibility issues between cards.

Lisa has said that they are committed to improving ROCm, especially for AI workloads. Recent releases (5.6/5.7) prove that.

Re: AMD may get across the CUDA moat

#69

Post by hot_gril »

People complain about Nvidia being anticompetitive with CUDA, but I don't really see it. They saw a gap in the standards for on-GPU compute and put tons of effort into a proprietary alternative. They tied CUDA to their own hardware, which sorta makes technical sense given the optimizations involved, but it's their choice anyway. They still support the open standards, but many prefer CUDA and will pay the Nvidia premium for it because it's actually nicer. They also don't have CPU marketshare to tie things to.

Good for them. We can hope the open side catches up either by improving their standards, or adding more layers like this article describes.

Re: AMD may get across the CUDA moat

#70

Post by mikepurvis »

bigcat12345678 wrote: post #44

Cuda is the foundation NVIDIA moat is the years of work built by oss community, big corporations, research insistute They spend all time building for cuda, a lot of implicit designs are derived from cuda's characteristic That will be the main challenge

It depends on the domain. Increasingly people's interfaces to this stuff are the higher level libraries like tensorflow, pytorch, numpy/cupy, and to a lesser degree accelerated processing libraries such as opencv, PCL, suitesparse, ceres-solver, and friends.

If you can add hardware support to a major library and improve on the packaging and deployment front while also undercutting on price, that's the moat gone overnight. CUDA itself only matters in terms of lock-in if you're calling CUDA's own functions.

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