3 ms·

One thing I don’t get is why AMD is not paying attention to the deep learning market. They could easily turn things around within a year with a team of 10 solid

by 0xbear 9y ago

One thing I don’t get is why AMD is not paying attention to the deep learning market. They could easily turn things around within a year with a team of 10 solid engineers, yet they seem to be choosing not to. I’m pretty sure the chips are capable of delivering the goods, it’s just a pain to get to the goods right now.

DannyBee 9y ago
There are way too many framework targets there be effective here yet. Any of these chips requires effective support from the frameworks, no matter what they do. Peanut buttering 10 engineers across all the frameworks to try to get market share is unlikely to work. You'd either need to dedicate an overwhelming team to shock and awe this (IE support and performance for AMD chips is best in class for all frameworks, period). This is 50 people, at least, who all work really well together, if you want to deliver it in the next year. Or, you can bide your time, dedicate those resources to better and better hardware, and when the frameworks field shakes out a bit, have a better shot. None of these people care about running on nvidia gpus (and in fact, the vendors pushing them don't want to be locked in either), so your main concern there is hand tuning and cuda kernel integration they do. The switching cost is something but not huge, and isn't increasing that much over time (unlike the x86 switching cost, for example). So waiting doesn't lose you a lot. So in the meantime, you make two bets: 1. You try to take over the intermediate IR of frameworks, and make it good enough that people stop writing hand tuned nvidia kernels. This is unlikely to work out, but worth a shot. 2. You slowly decide what customers you want, look at what they are writing hand-tuned kernels for, and try to tackle making the frameworks they use good enough to not need it. (in case #1 doesn't work out).
- 0xbear 9y ago
  Don’t peanut butter then. Put 3 on TF, 3 on PyTorch, and the remaining 4 on shared library of some sort. You’re right that doing 10 things at once is a recipe for failure, but the reality is, majority of frameworks don’t really matter all that much, and if a couple of solid integrations existed, they could just reuse that work on their own.
  - dragontamer 9y ago
    NVidia doesn't have just 3 or 4 engineers in these tasks. When you consider that NVidia just took Google's TPU concept, and basically cloned it into their upcoming GPU ("Tensor Cores"), you realize that NVidia is really pouring millions, maybe hundreds-of-millions, of dollars into AI. AMD has a number of initiatives that haven't panned out as well as NVidia's AI investment. AMD's "HSA" technology is actually quite interesting, although unpopular. AMD's push for HSA has made it faster at Video Rendering tasks (see Blender for instance) and random tasks like GPGPU-accelerated WinRAR decompression or LibreOffice Spreadsheets. IIRC, AMD Vega beats NVidia Titan XP in Creo and Solidworks benchmarks (CAD programs). So its not like AMD is sleeping on its laurels here, they're just focusing on other, still profitable, corners of the market. Of course, the current is behind AI, Tensors, and NVidia at the moment. AMD can't afford to fall further behind. The current trend is AI and Machine Learning, and it seems reasonable for AMD to at least get PyTorch running on AMD cards (if not "beating" NVidia, but at least they can play along).
    - sitkack 9y ago
      HSA is also a longer term play. The pieces are in place for AMD literally jump 1-2 years ahead of Intel and Nvidia.
  - DannyBee 9y ago
    I really don't think 3 people is going to cover this. Like literally, NVidia will just pay 3 people to do nothing but argue with those 3 people on mailing lists and keep them from landing patches at a reasonable rate. It would cost nothing compared to the benefit of slowing your only competitor down in a space worth this much. (and i've literally seen not-great companies do this to people's open source projects, so ...)
- make3 9y ago
  they don't need to target the frameworks themselves, just give optimized binaries/kernels for the operations. That's what Nvidia does with cudnn. the people building the frameworks will then wrap these building blocks. the real preoccupation is that these building blocks need to be as fast or faster than Nvidia's
  - dharma1 9y ago
    Exactly this. A handful of good AMD engineers should be able to squeeze everything out of their own hardware by tuning fast convolution, gemm and other ML related kernels optimised for Vega. That happens once, not for every framework. AMD's management just doesn't seem to be that interested in ML.
  - DannyBee 9y ago
    More likely, you do that, and won't bother. This is the difference between being #1 and #2 in a space. If what they have works for them, precisely none of these people will bother.
    - make3 9y ago
      I'm not sure. Cloud compute providers (Google, Amazon & MS) might write the wrappers just to try to give Nvidia competition in the gpu market and force them to let their prices. they have a huge incentive to go so

by 0xbear 9y ago

DannyBee 9y ago

There are way too many framework targets there be effective here yet. Any of these chips requires effective support from the frameworks, no matter what they do. Peanut buttering 10 engineers across all the frameworks to try to get market share is unlikely to work. You'd either need to dedicate an overwhelming team to shock and awe this (IE support and performance for AMD chips is best in class for all frameworks, period). This is 50 people, at least, who all work really well together, if you want to deliver it in the next year. Or, you can bide your time, dedicate those resources to better and better hardware, and when the frameworks field shakes out a bit, have a better shot. None of these people care about running on nvidia gpus (and in fact, the vendors pushing them don't want to be locked in either), so your main concern there is hand tuning and cuda kernel integration they do. The switching cost is something but not huge, and isn't increasing that much over time (unlike the x86 switching cost, for example). So waiting doesn't lose you a lot. So in the meantime, you make two bets: 1. You try to take over the intermediate IR of frameworks, and make it good enough that people stop writing hand tuned nvidia kernels. This is unlikely to work out, but worth a shot. 2. You slowly decide what customers you want, look at what they are writing hand-tuned kernels for, and try to tackle making the frameworks they use good enough to not need it. (in case #1 doesn't work out).

0xbear 9y ago
Don’t peanut butter then. Put 3 on TF, 3 on PyTorch, and the remaining 4 on shared library of some sort. You’re right that doing 10 things at once is a recipe for failure, but the reality is, majority of frameworks don’t really matter all that much, and if a couple of solid integrations existed, they could just reuse that work on their own.
- dragontamer 9y ago
  NVidia doesn't have just 3 or 4 engineers in these tasks. When you consider that NVidia just took Google's TPU concept, and basically cloned it into their upcoming GPU ("Tensor Cores"), you realize that NVidia is really pouring millions, maybe hundreds-of-millions, of dollars into AI. AMD has a number of initiatives that haven't panned out as well as NVidia's AI investment. AMD's "HSA" technology is actually quite interesting, although unpopular. AMD's push for HSA has made it faster at Video Rendering tasks (see Blender for instance) and random tasks like GPGPU-accelerated WinRAR decompression or LibreOffice Spreadsheets. IIRC, AMD Vega beats NVidia Titan XP in Creo and Solidworks benchmarks (CAD programs). So its not like AMD is sleeping on its laurels here, they're just focusing on other, still profitable, corners of the market. Of course, the current is behind AI, Tensors, and NVidia at the moment. AMD can't afford to fall further behind. The current trend is AI and Machine Learning, and it seems reasonable for AMD to at least get PyTorch running on AMD cards (if not "beating" NVidia, but at least they can play along).
  - sitkack 9y ago
    HSA is also a longer term play. The pieces are in place for AMD literally jump 1-2 years ahead of Intel and Nvidia.
- DannyBee 9y ago
  I really don't think 3 people is going to cover this. Like literally, NVidia will just pay 3 people to do nothing but argue with those 3 people on mailing lists and keep them from landing patches at a reasonable rate. It would cost nothing compared to the benefit of slowing your only competitor down in a space worth this much. (and i've literally seen not-great companies do this to people's open source projects, so ...)
make3 9y ago
they don't need to target the frameworks themselves, just give optimized binaries/kernels for the operations. That's what Nvidia does with cudnn. the people building the frameworks will then wrap these building blocks. the real preoccupation is that these building blocks need to be as fast or faster than Nvidia's
- dharma1 9y ago
  Exactly this. A handful of good AMD engineers should be able to squeeze everything out of their own hardware by tuning fast convolution, gemm and other ML related kernels optimised for Vega. That happens once, not for every framework. AMD's management just doesn't seem to be that interested in ML.
- DannyBee 9y ago
  More likely, you do that, and won't bother. This is the difference between being #1 and #2 in a space. If what they have works for them, precisely none of these people will bother.
  - make3 9y ago
    I'm not sure. Cloud compute providers (Google, Amazon & MS) might write the wrappers just to try to give Nvidia competition in the gpu market and force them to let their prices. they have a huge incentive to go so