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AMD Nearing Full OpenCL 2.0 Support With ROCm 2.0 Compute Stack
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Originally posted by Peter Fodrek View Post
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Originally posted by busukxuan View PostSorry if this is a stupid question, does "SVM" refer to support vector machines? Doesn't sound likely to me that OpenCL would have such a high level primitive.Test signature
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Originally posted by phoronix View Postit looks like AMD could be on a strong footing for GPU compute in 2019
I'm not cheerleading for Nvidia, but let's not kid ourselves. AMD seems to have walked away from HPC, and hasn't been competitive in deep learning since Nvidia's GP100 launched, in early 2016. That said, there will certainly be some applications where AMD can be competitive, but Nvidia's new RT cores just edged out another one.
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@coder: Nvidia started splitting their HPC and GPU lineups, they have been designing discrete top chips specifically for both graphics and compute starting with Pascal. Seems like AMD is doing the same now as they are preparing a HPC chip. It has new DL instructions, so maybe even specific hardware units similar to tensor cores. It has 1/2 FP64 rate, so it should reach Volta if they can manage to get past 1.8 GHz with a reasonable power budget, which I think is possible as they use TSMC's 7nm node and Vega10 already reaches 1.7 GHz.
I also think that RT has been a topic in the industry for a long time. Even Imagination Technologies managed to merge RT cores in their traditional GPU design. I think it might even be possible for AMD to get it inside Navi. At least, I hope so because I agree that dedicated hardware for RT is great and think it's the future of real time graphics as well. And it also helps very much in professional workloads.
I think their GPU team is still competitive. They just can't afford to release as many GPUs as Nvidia. As a reminder, in the past few years Nvidia has released GM200, GM204, GM206, GM207, GM208, GP100, GP102, GP104, GP106, GP107, GP108, GV100, and soon GT102, GT104, GT106. In the same timeframe, AMD has only released Tonga, Fiji, Polaris10, 11, 12, Vega10 and soon Vega20. And of course their CPU, APU and semi-custom SoCs. I think it's remarkable that they can keep up to this point and not being left behind much further.
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My layman's impression is that the big money in machine learning hardware, will initially be in large scale installations.
I am skeptical that cuda is the barrier to market entry it is credited with for AMD.
Writing some code doesnt bother those guys much if there is better perf/$ to be had from AMD.
The amd value proposition works quite well at scale. They dont mind much if it takes 3 AMD gpuS to do the work of 2 high priced nvidias, so long as its cheaper.
A recent game changer for AMD platform resources, is the explosion in core/thread numbers. That a ~humble TR can simply double cores using the same platform, seems a trick (~give each ccx a slave ccx on an ~easily modified die) that can be repeated on am4 and x399 epyc. Maybe AI will be a little less gpu centric now?
AMDs crushing dominance in most metrics, must make choosing a sibling amd gpu very compelling for epyc adopters
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Originally posted by coder View Post... let's not kid ourselves. AMD seems to have walked away from HPC, and hasn't been competitive in deep learning since Nvidia's GP100 launched, in early 2016. That said, there will certainly be some applications where AMD can be competitive, but Nvidia's new RT cores just edged out another one.
a champion team beats a team of champions.
would u say the same for multi 7nm Vega on a 64 core/128T Epyc in a tightly integrated ecosystem?
Which btw, has 128 lanes for scads of all important nvme.
IMO, using nand cleverly as pseudo ram, will be the key to the insatiable memory demands of vast data sets of big end of town AI.
Coders just use the resources available. If resource options change and improve - who knows what approach will yield the best AI perf/$? Its still a young field.
Last edited by msroadkill612; 11 September 2018, 06:45 AM.
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Originally posted by msroadkill612 View PostThere is never just one way to do things with code.
Originally posted by msroadkill612 View Posta champion team beats a team of champions.
Originally posted by msroadkill612 View Postwould u say the same for multi 7nm Vega on a 64 core/128T Epyc in a tightly integrated ecosystem?
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Originally posted by msroadkill612 View PostI am skeptical that cuda is the barrier to market entry it is credited with for AMD.
The other thing is to make sure those frameworks & surrounding toolchains are competitive with TensorRT, which is to deep learning what CUDA is to parallel programming APIs.
Originally posted by msroadkill612 View PostWriting some code doesnt bother those guys much if there is better perf/$ to be had from AMD.
The real problem is the numbers. AMD just has no answer for Nvidia's Tensor cores.
Originally posted by msroadkill612 View PostThe amd value proposition works quite well at scale. They dont mind much if it takes 3 AMD gpuS to do the work of 2 high priced nvidias, so long as its cheaper.
Originally posted by msroadkill612 View PostA recent game changer for AMD platform resources, is the explosion in core/thread numbers. That a ~humble TR can simply double cores using the same platform, seems a trick (~give each ccx a slave ccx on an ~easily modified die) that can be repeated on am4 and x399 epyc. Maybe AI will be a little less gpu centric now?
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Originally posted by juno View PostNvidia started splitting their HPC and GPU lineups, they have been designing discrete top chips specifically for both graphics and compute starting with Pascal.
Originally posted by juno View PostSeems like AMD is doing the same now as they are preparing a HPC chip. It has new DL instructions, so maybe even specific hardware units similar to tensor cores.
Originally posted by juno View PostIt has 1/2 FP64 rate,
Originally posted by juno View PostI think it might even be possible for AMD to get it inside Navi. At least, I hope so because I agree that dedicated hardware for RT is great and think it's the future of real time graphics as well. And it also helps very much in professional workloads.
I'm still left wondering where, exactly, they would put the RT cores. Their GPUs get less graphics performance per mm^2 than Nvidia's. Unless that situation changes, I don't see how they could justify burning extra silicon on RT hardware. They can't afford to release something that's unable to be cost-competitive with Nvidia. I think that's already happened, with Vega. Its price hasn't dropped to match current GeForce pricing. [checks current prices] ...although it might be getting close.
Originally posted by juno View PostThey just can't afford to release as many GPUs as Nvidia. As a reminder, in the past few years Nvidia has released GM200, GM204, GM206, GM207, GM208, GP100, GP102, GP104, GP106, GP107, GP108, GV100, and soon GT102, GT104, GT106. In the same timeframe, AMD has only released Tonga, Fiji, Polaris10, 11, 12, Vega10 and soon Vega20.
Also, you forgot Polaris20.
I think it will be painful for AMD not to release a new GPU, in 2018. The crypto-boom probably saved their bacon.
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