Yes, both are marketing and the reality is that GPUs are improving significantly slower than Moore’s law.
The time period I looked at in the graph is 2008-2016.
I don’t see how TOPS are relevant if GPUs still have the best price performance. I expect FLOPS and TOPS to scale linearly with each other for GPUs, i.e. if FLOPS increases by 2x in some time period then TOPS also will. (I could be wrong but this is the null hypothesis)
Regarding DGX-1 and DGX-2: You can’t extrapolate a medium-term trend from 2 data points 1 year apart like that, that’s completely absurd. Because DGX-2 only has 2 times the FLOPS of DGX-1 (while being 3x as expensive), I assume the 10x improvement is due to a discontinuous improvement in tensorization (similar to TPUs) that can’t be extrapolated.
GTX 280 (a 2008 card) is 620 GFLOPS, not 400. It cost $650 on release, and the card to compare it to (2017 Titan V; I think you meant the 2018 one but it doesn’t change things significantly) costs $3000. The difference in price performance is is 110000/620*650/3000 =38x over 9 years, slower than Moore’s law. We are talking about price performance not absolute performance here, since that is what this thread is about (economic/material constraints on growth of compute).
The signal to noise ratio in numbers in your comments is so low that I’m not trusting anything you’re saying and engaging further is probably not worth it.
Thanks for participating in interesting conversation which helped me to clarify my position.
As I now see, the accelerated growth, above Moore’s law level, started only around 2016 and is related not to GPU, which grew rather slowly, but is related to specialised hardware for neural nets, like Tensor cores, Google TPU and neuromorphic chips like True North and Akida. Neuromorphic chips could give higher acceleration for NNs than Tensor cores, but not yet hit the market.
Yes, both are marketing and the reality is that GPUs are improving significantly slower than Moore’s law.
The time period I looked at in the graph is 2008-2016.
I don’t see how TOPS are relevant if GPUs still have the best price performance. I expect FLOPS and TOPS to scale linearly with each other for GPUs, i.e. if FLOPS increases by 2x in some time period then TOPS also will. (I could be wrong but this is the null hypothesis)
Regarding DGX-1 and DGX-2: You can’t extrapolate a medium-term trend from 2 data points 1 year apart like that, that’s completely absurd. Because DGX-2 only has 2 times the FLOPS of DGX-1 (while being 3x as expensive), I assume the 10x improvement is due to a discontinuous improvement in tensorization (similar to TPUs) that can’t be extrapolated.
GTX 280 (a 2008 card) is 620 GFLOPS, not 400. It cost $650 on release, and the card to compare it to (2017 Titan V; I think you meant the 2018 one but it doesn’t change things significantly) costs $3000. The difference in price performance is is 110000/620*650/3000 =38x over 9 years, slower than Moore’s law. We are talking about price performance not absolute performance here, since that is what this thread is about (economic/material constraints on growth of compute).
The signal to noise ratio in numbers in your comments is so low that I’m not trusting anything you’re saying and engaging further is probably not worth it.
[EDIT: fixed the GTX 280 vs Titan V calculation]
Thanks for participating in interesting conversation which helped me to clarify my position.
As I now see, the accelerated growth, above Moore’s law level, started only around 2016 and is related not to GPU, which grew rather slowly, but is related to specialised hardware for neural nets, like Tensor cores, Google TPU and neuromorphic chips like True North and Akida. Neuromorphic chips could give higher acceleration for NNs than Tensor cores, but not yet hit the market.