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Radeon Instinct – Optimized Machine and Deep Learning

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Re: Radeon Instinct – Optimized Machine and Deep Learning

#21

Post by x0x0 »

madenine wrote: post #6
zitterbewegung wrote: post #2

Does anyone use AMD for deep learning in scientific / industry ? All the libraries for deep learning I have seen require CUDA and NVIDIA is winning by merely being the most popular API. Searching github it looks like they are university assignment projects see https://github.com/search?utf8=%E2%9C%93&q=opencl+deep+learn...

Some people do, but they're by far the minority. CUDA / cuDNN are well tuned and integrated with the most popular libraries. At this point, in order to get people to switch not only would your hardware have to be faster, but your replacement for cuDNN would have to be better as well. Which is by no means impossible, just difficult.

I disagree; most people use caffe / keras / theano / tensorflow / etc hiding the cuDNN details so end users won't care much. Offering more performance / shorter training loops is a big deal. Your typical ML learning loop with an expensive human regularly waiting for experiment results shows clear benefits.

Re: Radeon Instinct – Optimized Machine and Deep Learning

#22

Post by theandrewbailey »

slizard wrote: post #12

What's particularly interesting here is that the Fiji card they propose is a very different beast than any of the NVIDIA offerings. The MI8 card's HBM has a great power and performance advantage (512 GB/s peak bandwidth) even if it's on 28 nm. NVIDIA has nothing that has even remotely comparable bandwidth in this price/perf/TDP regime. None of the NVIDIA GP10[24] Teslas have GDDR5X -- not to surprising given that it…

So it's a headless passively cooled server version of the R9 Fury X? I figured this was a faster and improved version. Or is this Polaris based?

http://www.anandtech.com/show/9390/the-amd-radeon-r9-fury-x-...

Not sure if AMD is going to go all HBM on all it's high performance GPUs in 2017, or only offer one or two models with it.

http://arstechnica.com/gadgets/2016/03/amd-gpu-vega-navi-rev...

EDIT: MI6 is Polaris based, MI8 is Fiji with HBM, MI25 is Vega.

https://www.amd.com/en-us/press-releases/Pages/radeon-instin...

Re: Radeon Instinct – Optimized Machine and Deep Learning

#23

Post by slizard »

dogma1138 wrote: post #16
slizard wrote: post #13

Earlier quoted context omitted.

> can't interop the "operational" CUDA features What exactly do you that mean?

DMA, networking, storage and direct i/o, virtualization etc.

The tricky, but fun parts. It's certainly not trivial to port those, but I'm not too worried about it as long as there is solid runtime support in ROCm. More fun work for perf engineers like me. ;)

Massive amount of boilerplate, heavy APIs or crappy software stack are more dangerous IMO than having to drop in replacements for NVIDIA-specific optimizations of GPU-to-NIC or GPU-to-GPU communication.

Re: Radeon Instinct – Optimized Machine and Deep Learning

#24

Post by jsheard »

slizard wrote: post #12

What's particularly interesting here is that the Fiji card they propose is a very different beast than any of the NVIDIA offerings. The MI8 card's HBM has a great power and performance advantage (512 GB/s peak bandwidth) even if it's on 28 nm. NVIDIA has nothing that has even remotely comparable bandwidth in this price/perf/TDP regime. None of the NVIDIA GP10[24] Teslas have GDDR5X -- not to surprising given that it…

Those MIOpen benchmarks are a bit dubious, since MIOpen is AMDs own deep learning framework. It's unlikely that code written by AMD is optimal for the Nvidia hardware.

To be realistic you need to compare AMD hardware running MIOpen to NV hardware running a framework backed by cuDNN.

Re: Radeon Instinct – Optimized Machine and Deep Learning

#25

Post by ipunchghosts »

This really doesnt matter for deep learning. There is a large ecosystem built around CUDA. Unless AMD becomes CUDA compatible (they are working on it but not there yet) and I can install Torch/TF and run it on my AMD GPU, I will stick with NVIDIA.

I am all for choice, but AMD has a lot of catching up to do.

Re: Radeon Instinct – Optimized Machine and Deep Learning

#26

Post by visarga »

mrlatinos wrote: post #17
raj_m wrote: post #7

I really don't think this will make a dent in CUDA's platform. CUDA has a well established ecosystem in deep learning and compatible cards like Quadro coupled with very matured platform makes it miles ahead of platform. That said, I would love to be proven wrong. Healthy competition such as this fosters much better results. Also CUDA is not without issues in certain matters.

Just speaking from a personal perspective, I took a parallel computing course at my uni this past semester and CUDA was the main platform we worked on (and I'm an undergraduate). Nvidia also has a great Udacity course they offer for free. Unless AMD gets CUDA compatibility working soon, I really don't see how they're going to catch up as far as adoption goes.

They either get CUDA or compatibility with all major frameworks. And they'd better be cheaper or faster than NVIDIA.

Re: Radeon Instinct – Optimized Machine and Deep Learning

#27

Post by deepnotderp »

zitterbewegung wrote: post #2

Does anyone use AMD for deep learning in scientific / industry ? All the libraries for deep learning I have seen require CUDA and NVIDIA is winning by merely being the most popular API. Searching github it looks like they are university assignment projects see https://github.com/search?utf8=%E2%9C%93&q=opencl+deep+learn...

They desperately need a cudnn competitor.

Re: Radeon Instinct – Optimized Machine and Deep Learning

#28

Post by slizard »

theandrewbailey wrote: post #22
slizard wrote: post #12

What's particularly interesting here is that the Fiji card they propose is a very different beast than any of the NVIDIA offerings. The MI8 card's HBM has a great power and performance advantage (512 GB/s peak bandwidth) even if it's on 28 nm. NVIDIA has nothing that has even remotely comparable bandwidth in this price/perf/TDP regime. None of the NVIDIA GP10[24] Teslas have GDDR5X -- not to surprising given that it…

So it's a headless passively cooled server version of the R9 Fury X? I figured this was a faster and improved version. Or is this Polaris based? http://www.anandtech.com/show/9390/the-amd-radeon-r9-fury-x-... Not sure if AMD is going to go all HBM on all it's high performance GPUs in 2017, or only offer one or two models with it. http://arstechnica.com/gadgets/2016/03/amd-gpu-vega-navi-rev... EDIT: MI6 is Polaris bas…

> So it's a headless passively cooled server version of the R9 Fury X? I figured this was a faster and improved version.

Almost. My guess is R9 Nano [1] given the same 8.2 Tflops (SP) Flop rate [2].

[1] http://www.anandtech.com/show/9621/the-amd-radeon-r9-nano-re... [2] http://images.anandtech.com/doci/10905/AMD%20Radeon%20Instin...

> Not sure if AMD is going to go all HBM on all it's high performance GPUs in 2017, or only offer one or two models with it.

It would make perfect sense to have some GDDR5X-based medium-range GPUs. HBM2 will be expensive, too expensive for the top of the medium range (and the same applies for NVIDIA). GDDR5X has plenty of room for improvement over GDDR5 and by next year they should have it figured out better.

Re: Radeon Instinct – Optimized Machine and Deep Learning

#29

Post by dogma1138 »

slizard wrote: post #23
dogma1138 wrote: post #16

Earlier quoted context omitted.

DMA, networking, storage and direct i/o, virtualization etc.

The tricky, but fun parts. It's certainly not trivial to port those, but I'm not too worried about it as long as there is solid runtime support in ROCm. More fun work for perf engineers like me. ;) Massive amount of boilerplate, heavy APIs or crappy software stack are more dangerous IMO than having to drop in replacements for NVIDIA-specific optimizations of GPU-to-NIC or GPU-to-GPU communication.

Not only not trivial to port those but they are constantly will have to play catch up and be at the mercy of NVIDIA in regards to spec.

AMD is in a catch 22, support CUDA and be effectively in a constant catch up position, not support it and continue to be ignored by the market at large simply because the momentum NVIDIA has managed to achieve with CUDA over the years.

Re: Radeon Instinct – Optimized Machine and Deep Learning

#30

Post by Twirrim »

rsp1984 wrote: post #14

Interesting that NVDA is down almost 4% for the day [1] while AMD is up 3% [2]. Is Wall Street realizing that NVidia is not alone in the ML Hardware space? [1] https://www.google.com/finance?q=NASDAQ:NVDA [2] https://www.google.com/finance?q=NASDAQ%3AAMD

It's way too early to be making guesses like that.

Micro-trends are mostly meaningless with stocks, unless you're trying to do high frequency work. Stock shifts and changes to small extents all the time based on the quirks of all sorts of trading companies, and each of Especially if there hasn't been any significant news about the company, and this isn't significant news. It's interesting, but it isn't really threatening Nvidia's dominance or profits at the moment. AMD needs to make a bigger name for itself in the sector, start picking up some splashy customers before most of the market might react.

Looking at the bigger picture, Nvidia stock is up 160% so far this year, but has been fluctuating a bunch over the last month or so, and it's still well within the scope of those fluctuations.

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