4 ms·

This is a common sentiment, no doubt also driven by the wish thay AMD would cater to us. But I see no evidence that the strategy is wrong or failing. AMD is al

This is a common sentiment, no doubt also driven by the wish thay AMD would cater to us. But I see no evidence that the strategy is wrong or failing. AMD is already powering a massive and rapidly growing share of Top 500 HPC: https://www.top500.org/statistics/treemaps/ https://www.top500.org/statistics/treemaps/ AMD compute growth isn't in places where people see it, and I think that gives a wrong impression. (Or it means people have missed the big shifts over the last two years.)

Jlagreen 2y ago
Super computers are in 95% cases government funded and I recommend that you check in conditions for tenders and how government has check on certain condition in buying. That isn't a normal business partner who only looks at performance, there are many more other criteria in the descision making. Or let me ask you directly, can you name me one enterprise which would buy a super computer and wait 5+ years for it and fund the development of HW for it which doesn't exist yet? At the same time when the competition can deliver a super computer within the year with an existing product? No sane CEO would have done Frontier or El Capitan. Such things work only with government funding where the government decides to wait and fund an alternative. But AMD is indeed a bit lucky that it happened or otherwise they wouldn't been forced to push the Instinct line. In the commercial world, things work differently. There is always a TCO calculation. But one critical aspect since the 90s is SW. No matter how good the HW is, the opportunity costs in SW could force enterprises to use the inferior HW due to SW deployment. If vision computing SW in industry is supporting and optimized for CUDA or even runs only with CUDA then any competition has a very hard time penetrating that market. They first have to invest a lot of money to make their products equally appealing. AMD makes a huge mistake and is by far not paranoid enough to see it. For 2 decades, AMD and Intel have been in a nice spot with PC and HPC computing requiring x86. It basically to this date has guaranteed a steady demand. But in that timeframe mobile computing has been lost to ARM. ML/AI doesn't require x86 as Nvidia demonstrates by combining their ARM CPUs into the mix but also ARM themselves want more and more of the PC and HPC computing cake. And MS is eager to help with OS for ARM solutions. What that means is that if some day x86 isn't as dominant anymore and ARM becomes equally good then AMD/Intel will suddenly have more competition in CPUs and might even offer non-x86 solutions as well. Their position will therefore drop into yet another commodity CPU offering. In the AI accelerator space we will witness something similiar. Nvidia has created a platform and earns tons of money with it by combining and optimizing SW+HW. Big Tech is great at SW but not yeat at HW. So the only logical thing to do is getting better at HW. All large Tech companies are working on their own accelerators and they will build their platform around it to compete with Nvidia and locking in customers all the same way. The primary losers in all of this will be HW only vendors without a platform, hoping that Big Tech will support them on their platforms. Amazon and Google have already shown today that they have no intention to support anything besides their platform and Nvidia (which they only must due to customer demand).
- latchkey 2y ago
  I am that crazy ceo building a super computer, for rent by anyone who wants it. We are starting small and growing with demand. Our first deployment is 3x larger flops than Cheyenne and a fraction of the cost. https://en.wikipedia.org/wiki/Cheyenne_(supercomputer) https://en.wikipedia.org/wiki/Cheyenne_(supercomputer)
frognumber 2y ago
I see a lot of evidence, in the form of a rising moat for NVidia.
jampekka 2y ago
It would be interesting to see how much these "supercomputers" are actually used, and what parts of them are used. I use my university's "supercomputer" every now and then when I need lots of VRAM, and there are rarely many other users. E.g. I've never had to queue for a GPU even though I use only the top model, which probably should be the most utilized. Also, I'd guess there can be nvidia cards in the grid even if "the computer" is AMD. Of course it doesn't matter for AMD whether the compute is actually used or not as long as it's bought, but lots of theoretical AMD flops standing somewhere doesn't necessarily mean AMD is used much for compute.
- roenxi 2y ago
  It is a pretty safe bet that if someone builds a supercomputer there is a business case for it. Spending big on compute then leaving it idle is terrible economics. I agree with Certhas in that although this is not a consumer-first strategy it might be working. AMDs management are not incapable, for all that they've been outmanoeuvred convincingly by Nvidia. That being said, there is a certain irony and schadenfreude in the AMD laptop being bricked from the thread root. The AMD engineers are at least aware that running a compute demo is an uncomfortable experience on their products. The consumer situation is not acceptable even if strategically AMD is doing OK.
  - jampekka 2y ago
    I find it a safer bet that there are terrible economics all over. Especially when the buyers are not the users, as is usually the case with supercomputers (just like with all "enterprise" stuff). In the cluster I'm using there's 36 nodes, of which 13 are currently not idling (doesn't mean they are computing). There are 8 V100 GPUs and 7 A100 GPUs and all are idling. Admittedly it's holiday season and 3AM here, but this it's similar other times too. This is of course great for me, but I think the safer bet is that the typical load average of a "supercomputer" is under 0.10. And the less useful the hardware, the less will be its load.
    - jfkfif 2y ago
      It is not a reasonable assumption to compare your local cluster to the largest clusters within DOE or their equivalents in Europe/Japan. These machines regularly run at >90% utilization and you will not be given an allocation if you can’t prove that you’ll actually use the machine. I do see the phenomenon you describe on smaller university clusters, but these are not power users who know how to leverage HPC to the highest capacity. People in DOE spend their careers working to use as much as these machines as efficiently as possible.
deleted 2y ago
[deleted]

Jlagreen 2y ago

Super computers are in 95% cases government funded and I recommend that you check in conditions for tenders and how government has check on certain condition in buying. That isn't a normal business partner who only looks at performance, there are many more other criteria in the descision making. Or let me ask you directly, can you name me one enterprise which would buy a super computer and wait 5+ years for it and fund the development of HW for it which doesn't exist yet? At the same time when the competition can deliver a super computer within the year with an existing product? No sane CEO would have done Frontier or El Capitan. Such things work only with government funding where the government decides to wait and fund an alternative. But AMD is indeed a bit lucky that it happened or otherwise they wouldn't been forced to push the Instinct line. In the commercial world, things work differently. There is always a TCO calculation. But one critical aspect since the 90s is SW. No matter how good the HW is, the opportunity costs in SW could force enterprises to use the inferior HW due to SW deployment. If vision computing SW in industry is supporting and optimized for CUDA or even runs only with CUDA then any competition has a very hard time penetrating that market. They first have to invest a lot of money to make their products equally appealing. AMD makes a huge mistake and is by far not paranoid enough to see it. For 2 decades, AMD and Intel have been in a nice spot with PC and HPC computing requiring x86. It basically to this date has guaranteed a steady demand. But in that timeframe mobile computing has been lost to ARM. ML/AI doesn't require x86 as Nvidia demonstrates by combining their ARM CPUs into the mix but also ARM themselves want more and more of the PC and HPC computing cake. And MS is eager to help with OS for ARM solutions. What that means is that if some day x86 isn't as dominant anymore and ARM becomes equally good then AMD/Intel will suddenly have more competition in CPUs and might even offer non-x86 solutions as well. Their position will therefore drop into yet another commodity CPU offering. In the AI accelerator space we will witness something similiar. Nvidia has created a platform and earns tons of money with it by combining and optimizing SW+HW. Big Tech is great at SW but not yeat at HW. So the only logical thing to do is getting better at HW. All large Tech companies are working on their own accelerators and they will build their platform around it to compete with Nvidia and locking in customers all the same way. The primary losers in all of this will be HW only vendors without a platform, hoping that Big Tech will support them on their platforms. Amazon and Google have already shown today that they have no intention to support anything besides their platform and Nvidia (which they only must due to customer demand).

latchkey 2y ago
I am that crazy ceo building a super computer, for rent by anyone who wants it. We are starting small and growing with demand. Our first deployment is 3x larger flops than Cheyenne and a fraction of the cost. https://en.wikipedia.org/wiki/Cheyenne_(supercomputer) https://en.wikipedia.org/wiki/Cheyenne_(supercomputer)

frognumber 2y ago

I see a lot of evidence, in the form of a rising moat for NVidia.

jampekka 2y ago

It would be interesting to see how much these "supercomputers" are actually used, and what parts of them are used. I use my university's "supercomputer" every now and then when I need lots of VRAM, and there are rarely many other users. E.g. I've never had to queue for a GPU even though I use only the top model, which probably should be the most utilized. Also, I'd guess there can be nvidia cards in the grid even if "the computer" is AMD. Of course it doesn't matter for AMD whether the compute is actually used or not as long as it's bought, but lots of theoretical AMD flops standing somewhere doesn't necessarily mean AMD is used much for compute.

roenxi 2y ago
It is a pretty safe bet that if someone builds a supercomputer there is a business case for it. Spending big on compute then leaving it idle is terrible economics. I agree with Certhas in that although this is not a consumer-first strategy it might be working. AMDs management are not incapable, for all that they've been outmanoeuvred convincingly by Nvidia. That being said, there is a certain irony and schadenfreude in the AMD laptop being bricked from the thread root. The AMD engineers are at least aware that running a compute demo is an uncomfortable experience on their products. The consumer situation is not acceptable even if strategically AMD is doing OK.
- jampekka 2y ago
  I find it a safer bet that there are terrible economics all over. Especially when the buyers are not the users, as is usually the case with supercomputers (just like with all "enterprise" stuff). In the cluster I'm using there's 36 nodes, of which 13 are currently not idling (doesn't mean they are computing). There are 8 V100 GPUs and 7 A100 GPUs and all are idling. Admittedly it's holiday season and 3AM here, but this it's similar other times too. This is of course great for me, but I think the safer bet is that the typical load average of a "supercomputer" is under 0.10. And the less useful the hardware, the less will be its load.
  - jfkfif 2y ago
    It is not a reasonable assumption to compare your local cluster to the largest clusters within DOE or their equivalents in Europe/Japan. These machines regularly run at >90% utilization and you will not be given an allocation if you can’t prove that you’ll actually use the machine. I do see the phenomenon you describe on smaller university clusters, but these are not power users who know how to leverage HPC to the highest capacity. People in DOE spend their careers working to use as much as these machines as efficiently as possible.

deleted 2y ago

[deleted]