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The Nvidia DGX-1 Deep Learning Supercomputer in a Box
- chm 10y agoAny idea how much this costs?
- dazzeruk 10y agoThe Nvidia slides had it at $129,000 a pop
- rckclmbr 10y agoWow, cheap, great for startups!
- showerst 10y agoAs of last year prices for general HPC resources were running around $3/GFLOP[1], or about $500,000 for 170TFlops if my math is correct. Sounds like this is a significant cost savings if it fits your use case.
- maaku 10y agoUh, using what hardware? The 980 Ti is about 11 TFLOP in half-precision (apples to apples). So 16x 980 Ti cards would take up twice as much rack space for $11k. Your estimate (and NVIDIA's pricing) is off by more than an order of magnitude...
- wmf 10y agoIsn't the ECC tax around 10x?
- marshray 10y agoSo, just do the computation twice and compare the results? OK, so there's twice the power to pay for but it seems like at $129k acquisition cost per 3.2KW consumption you could run for tens of years before break-even.
- mon_insider 10y agoA 980 Ti doesn't have FP16 hardware. The only Maxwell based component with such support is their Tegra part.
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- dharma1 10y agoI'll take 5
- sp332 10y agoWow, I didn't realize they were shipping HBM2 already. 720GB/s - with only 16GB of RAM, you can read it all in 22 milliseconds!
- DeepYogurt 10y agoThey're not. All that was mentioned in the talk was that this chip is coming soon.
- sp332 10y agoThere's a big green "Order Now" button about 1/3 of the way down the page.
- DeepYogurt 10y agoAnd no delivery date.
- Coding_Cat 10y agoWait, how many chips did they cram in there that they're getting 170 TFlops. Even at a very generous 10 TFLOP per chip that would be 17 chips.
- krasin 10y agoNVIDIA Tesla P100 has 21 TeraFLOPS of FP16 performance by their words. So they got 8 chips there.
- jsheard 10y agoYep, they showed a diagram of how it fits together: http://i.imgur.com/xk1daFG.jpg http://i.imgur.com/xk1daFG.jpg
- cptskippy 10y agoI wish that made that information more accessible. I wasn't able to find it on the site and it was all I really cared about.
- aconz2 10y agohttps://devblogs.nvidia.com/parallelforall/wp-content/uploads/2016/04/8-GPU-hybrid-cube-mesh-624x424.png https://devblogs.nvidia.com/parallelforall/wp-content/upload... source: https://devblogs.nvidia.com/parallelforall/inside-pascal/ https://devblogs.nvidia.com/parallelforall/inside-pascal/
- Coding_Cat 10y agoAh, half-floats. That explains it. Still pretty high but realistic at least.
- aconz2 10y agoCheck out the specs here: http://images.nvidia.com/content/technologies/deep-learning/pdf/61681-DB2-Launch-Datasheet-Deep-Learning-Letter-WEB.pdf http://images.nvidia.com/content/technologies/deep-learning/... though I'm most curious about what motherboard is in there to support NVLink and NVHS. Good overview of Pascal here: https://devblogs.nvidia.com/parallelforall/inside-pascal/ https://devblogs.nvidia.com/parallelforall/inside-pascal/ 1 question: will we see NVLink become an open standard for use in/with other coprocessors? 1 gripe: they give relative performance data as compared to a CPU -- of course its faster than a CPU
- virtuallynathan 10y agoIt looks like it uses a separate daughterboard that houses the GPUs + NVLink, connected to the main motherboard using quad Infiniband EDR (400Gbps) + RDMA. http://images.anandtech.com/doci/10225/SSP_85.JPG http://images.anandtech.com/doci/10225/SSP_85.JPG
- pinewurst 10y agoThe diagram is confusing, but the GPUs are connected to the NVLink matrix which is connected to the motherboard via the PLX PCIe switches. The quad IB/dual 10GbE are separate IO attached to the motherboard. https://devblogs.nvidia.com/parallelforall/inside-pascal/ https://devblogs.nvidia.com/parallelforall/inside-pascal/
- virtuallynathan 10y agoThat would make much more sense. Thanks! The PCI bandwidth must be fairly limited. 4x 100G Infiniband is 64x PCIe lanes, out of 80x lanes available.
- dgacmu 10y agoYou mean you're not surprised that a machine with 8 GPUs, apparently costing $129k USD (from comment below), can outperform a single CPU? :) (Of course, a better metric is that it's getting ~56x the performance at probably ~10x the TDP, but that's not surprising for a GPU with the current state of deep learning code.) To their credit, the thermal and power engineering needed to get that dense a compute deployment is challenging. (bt, dt, have the corpses of power supplies to show for it.) But the price means that it's going to be limited to hyper-dense HPC deployments by companies that don't have the resources to engineer their own for substantially less money, such as Facebook's Big Sur design: https://code.facebook.com/posts/1687861518126048/facebook-to-open-source-ai-hardware-design/ https://code.facebook.com/posts/1687861518126048/facebook-to... . And, of course, the academics and hobbyists will continue to use consumer GPUs , which give much better performance/$ but aren't nearly as HPC-friendly.
- jra101 10y agoMore detail on the GPUs in the system: https://devblogs.nvidia.com/parallelforall/inside-pascal/ https://devblogs.nvidia.com/parallelforall/inside-pascal/
- Robadob 10y agoHave they published a copy of the video of the autonomous car trained with unsupervised learning from the keynote anywhere? I'd love to show it to my father.
- phelm 10y agoI am looking forward to OpenCL catching up with CUDA in maturity and adoption, so that NVidia's monopoly in Silicon for deep learning will come to an end.
- DeepYogurt 10y agoMe too. I really want to see some benchmarks between cuda code and opencl code generated from cuda with AMDs compiler. Actually if anyone has a geforce/tesla get on this!
- Robadob 10y agoI haven't seen any recent benchmarks, but ones from 2011 all seemed to show CUDA and OpenCL on open footing in terms of performance when optimised properly.[1][2] CUDA simply had better library support, and a more well defined and uniform architecture to target. Whereas OpenCL is likely to require more programming to fill in the gaps for library support, and different optimisations depending on the architecture you wish to target. I'm guessing since then, the CUDA compiler may have improved somewhat in terms of optimisation based on some micro-benchmarking research I was looking the at the other day. There's also Intel's MIC to consider now to, although that has a vastly different architecture to GPU. Again performance was similar between MIC and GPU in 2013[3], each performing better where their architecture was more suited, GPUs were capable of providing double the bandwidth for random access data. In terms of AMD vs NVIDIA, I've not looked into it, I doubt AMD has anything to really compete with NVIDIAs current GPU accelerated compute lines. However again there was always that distinction (re bitcoin?) that AMD cards have better integer arithmetic and NVIDIA better float arithmetic. Disclaimer: I use CUDA in my research, never tried OpenCL. [1] http://arxiv.org/abs/1005.2581 http://arxiv.org/abs/1005.2581 [2] http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6047190&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6047190 http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=604719... [3] http://arxiv.org/abs/1311.0378 http://arxiv.org/abs/1311.0378
- DeepYogurt 10y agoWould you mind trying the AMD compiler? http://gpuopen.com/compute-product/hip-convert-cuda-to-portable-c-code/ http://gpuopen.com/compute-product/hip-convert-cuda-to-porta... I'd be interested in seeing a benchmark between some original cuda code and the opencl output of this compiler on the same gpu.
- partycoder 10y agoCosts $129,000 and needs 3.2 kilowatts to run.
- olympus 10y agoTo me, $129k isn't surprising since it is only going to be bought by researchers with big budgets. Small-timers will still build 3x GTX980 systems for under $5k. 3.2 KILOwatts sounded insane to me, but I suppose you'll have your own server rack to put it in if you can afford to buy one of these.
- TkTech 10y ago3.2kw isn't that insane considering what you're getting out of it. A coffee pot is 1kw, a toaster is 1.2kw, an electric broiler is 3.6kw. Running costs would be a very tiny part of any budget. Ends up being $9.216/day assuming peak costs, peak usage, and 24h operation.
- astrodust 10y agoIf that sounds insane, you're going to lose your mind when you realize how many KILOwatts your oven uses. 3.2KW is less than a dishwasher.
- nkurz 10y agoAre you sure your numbers are right? What kind of dishwasher do you have? And what kind of oven? For the US, at least, most dishwashers are well under 1600W, and few ovens exceed under 3200W. https://www.daftlogic.com/information-appliance-power-consumption.htm https://www.daftlogic.com/information-appliance-power-consum...
- witty_username 10y agoYeah, 3.2 KW would mean the dishwasher's heating the dishes to high temperatures, so it'd be more of an oven than a dishwasher.
- ansible 10y agoThe unified memory architecture with the Pascal GP100 is pretty sweet. That will make it easier to work with large data sets.
- AndrewKemendo 10y agoHow does this compare to some of the systems provided by cloud providers? Seems like requiring an on-site capability is a hurdle for integration if you already have your data on a cloud provider. [1] https://aws.amazon.com/machine-learning/ https://aws.amazon.com/machine-learning/ [2] https://azure.microsoft.com/en-us/services/machine-learning/ https://azure.microsoft.com/en-us/services/machine-learning/
- jdcarter 10y agoI would argue that this box is probably targeted at cloud providers. The Nvidia GRID boards are similar--they're not for consumers, but for GPU/Gaming-as-a-service providers.
- bpires 10y agoI wonder how much faster the new Tesla P100 is compared to the Tesla K40 in training neural networks. The K40s were the best available GPUs for training deep neural networks.
- madengr 10y agoNote the P100 is 20 Tflops for half precision (16 bit). For general purpose GPU (I use them for EM simulation) I assume one would want 32-bit, which is 10 Tflops. But still looks much much better for 64-bit computations than the previous generation
- pareci 10y agoCurious. Why do you post here when every other comment is random posturing?
- madengr 10y agoThey were touting 20 Tflops, but that's only for FP16, which isn't useful for many engineering computations that use GPU. I already can hit 2 Tflop F32 with two K20. It's a nice improvement over what I have now, but nothing astronomical.
- dharma1 10y agowas hoping they would announce Pascal GTX's. Oh well. Computex I guess
- manav 10y ago$129k for this machine. In the keynote its interesting that they mentioned the product line being: "Tesla M40 for hyperscale, K80 for multi-app HPC, P100 for scales very high, and DGX-1 for the early adopters". The GP100/P100 with the 16nm process probably gives a considerable performance/power advantage over the Tesla... but this gives me the feeling that we may not see consumer or workstation-level Pascal boards for a while.
- blakes 10y ago$129k seem extremely fair for what you get actually, in my experience.
- svensken 10y agoI was wondering about this too, the way they plugged old K80's at the end for non-deep-learning applications. Either they're clever about keeping multiple product lines alive (more profits!) or it's a big cop-out (they're hiding something about P100 that makes it a bad choice for GPGPU - maybe price?)
- dougmany 10y agoThis announcement reminds me of the part of Outliers that spelled out how Bill Gates and others became who they are because they had access to very expensive equipment before anyone else did (and spent 10K hours on it).
- aperrien 10y agoDoes anyone know if the Pascal architecture is built using stacked cores? Or is this one of those applications where thermal problems keep that technique from being used?
- wmf 10y agoNo, the Pascal GPU itself is not stacked. Die stacking makes almost no sense for processors.
- nshm 10y agoLooks like a research in machine learning will only be done in huge corporations. You'll need an amount of funding comparable to LHC. Time to use better models like kernel ensembles, maybe they are not that accurate, but they are easier to train on a single CPU.
- Houshalter 10y agoYou can already do deep learning on cheap consumer hardware. And $100k is expensive, but it's nowhere near LHC levels.
- Fomite 10y agoThis price point is extremely accessible to most major research universities as well.
- mortenjorck 10y agoJust for some perspective, a little over 10 years ago, this $130k turnkey installation would sit at #1 in TOP500, easily beating out hundred-million-dollar initiatives like NEC's Earth Simulator and IBM's BlueGene/L: http://www.top500.org/lists/2005/06/ http://www.top500.org/lists/2005/06/ (170 TFLOPS vs. 137 TFLOPS) At the other end, even a single GTX 960 would make it onto the list, placing in the 200s.
- lern_too_spel 10y agoThat is 170 TFLOPS Rpeak (theoretical performance assuming you could find a workload that doesn't need to wait for data movement) at half precision vs. 137 TFLOPS Rmax (usable performance on a dummy linear algebra problem) at double precision. No, it would not top the list.
- trsohmers 10y agoThe 170 TFLOPs number that NVIDIA gives out is for FP16, while the Top 10 list gives its number for for FP64. The P100 that makes up this NVIDIA box gives about 5.3TFLOPs per card, or a total of 42.4TFLOPs for the whole box. Sure, you can say that deep learning doesn't need FP64, but it is REALLY unfair to compare this to anything on the TOP500 list, especially when you consider the fact that this is not balanced in terms of memory size or bandwidth (in relation to the number of FLOPs) when you compare it to any real supercomputer class system.
- doyoulikeworms 10y agoOut of curiosity, what are some problems/solutions that require FP64?
- zevets 10y agoAny finite element/volume problem. Anything integrated.
- jlebar 10y agoStill, how many of these boxes are we talking about to match the performance of the #1 from the top 500 in 2005? 10? 20? That's still under $3m for 20, which is pretty impressive to me.
- pmorici 10y agoAnyone have any idea of how the GPUs in this machine compare to the GPUs in their high end gaming products?
- 0x07c0 10y agoTesla's has more Double Float cores compared to gaming cards.
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- caycep 10y agoDoes that mean Pascal release is just around the corner?!? -unreformed box builder
- agumonkey 10y agoWhat a peculiar pascaline.
- visarga 10y agoIt's good to see powerful machine learning hardware come out. Much of the progress in ML has come from hardware speedup. It will empower the next years of research.
- intrasight 10y agoIs also fun to contemplate that in about five years you'll likely be able to buy one of these on eBay for about $10K.
- nickpeterson 10y agoI have to wonder about intel and their Xeon Phi range. Last I checked they were supposed to launch a followup late last year that never manifested. Now we're 4 months in 2016 and still no new phi's. Couple that with the fact that they want you to use their compilers (extremely expensive), on a specialized system that can support the card, and you get a platform that nobody other than supercomputer companies can reasonably use. Meanwhile any developer who want to try something with cuda can drop $200 dollars on a GPU and go, then scale accordingly. I think intel somewhat acknowledged this by having a firesale on phi cards and dev licenses last year but it was only for a passively cooled model (really only works well in servers, not workstations). Intel do this: - Offer a $200-400 XEON PHI CARD - Include whatever compiler needed to use it with the card - Make this easily buyable - Contribute ports of Cuda-based frameworks over to Xeon Phi I feel like they could do this pretty easily, even if it lost money, it's pennies compared to what they're going to lose if nvidia keeps trumping them on machine learning. They need to give dev's the tooling and financial incentive to write something for Phi instead of cuda, right now it completely doesn't exist and frameworks basically use Cuda by default. If you're AMD, do the same thing but replace the phrase Xeon Phi with Radeon/Firepro