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The Nvidia DGX-1 Deep Learning Supercomputer in a Box

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Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box

#61

Post by wmf »

maaku wrote: post #49
showerst wrote: post #13

Earlier quoted context omitted.

As 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.

Uh, 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...

Isn't the ECC tax around 10x?

Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box

#62

Post by madengr »

pareci wrote: post #43
madengr wrote: post #26

Note 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

Curious. Why do you post here when every other comment is random posturing?

They 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.

Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box

#63

Post by Houshalter »

nshm wrote: post #59

Looks 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.

You can already do deep learning on cheap consumer hardware. And $100k is expensive, but it's nowhere near LHC levels.

Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box

#64

Post by astrodust »

dgacmu wrote: post #15
aconz2 wrote: post #9

Check out the specs here: 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/ 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 CP…

You 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…

$129K buys you a lot of dual 22-core servers.

Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box

#65

Post by astrodust »

olympus wrote: post #39
badminton1 wrote: post #21

Costs $129,000 and needs 3.2 kilowatts to run.

To 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.

If 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.

Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box

#66

Post by nightski »

olympus wrote: post #35
nightski wrote: post #27

Earlier quoted context omitted.

What hardware could OpenCL even run on that would come remotely close to what this system has to offer?

OpenCL runs on Nvidia GPUs, so you could do an apples to apples comparison on this system.

Fair enough but how does that further the goal of having alternatives to Nvidia? The truth is the problem isn't cuda vs. opencl. There simply aren't good alternatives to the hardware Nvidia provides. If the hardware was there, you'd see people switch to OpenCL.

Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box

#67

Post by [deleted] »

make3 wrote: post #36
bgalbraith wrote: post #31

Earlier quoted context omitted.

NVIDIA does not have a monopoly in the traditional sense. But yes, the have a de facto one because there is no viable competition. It's like saying MATLAB has a monopoly in academic research because so much of the code is written in it. That is slowly changing and moving over to Python now, which is great. Maybe OpenCL will get there someday, but I don't see it happening any time soon.

this is wrong. No mainstream deep learning library uses openCL, and the non-mainstream ones that do are much much slower. I remember reading up to 10x slower, but I can't seem to find the reference right now.

[deleted]

Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box

#68

Post by mortenjorck »

Just 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/ (170 TFLOPS vs. 137 TFLOPS)

At the other end, even a single GTX 960 would make it onto the list, placing in the 200s.

Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box

#69

Post by mon_insider »

DeepYogurt wrote: post #40
Robadob wrote: post #32

Earlier quoted context omitted.

I 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 w…

Would you mind trying the AMD compiler? 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.

I don't think CUDA vs OpenCL is the real issue. It's the libraries that come with them. It matters little in which language they were written, since they are closed source for the most part.

AMD's Boltzmann initiative won't solve the lack of libraries.

Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box

#70

Post by mon_insider »

maaku wrote: post #49
showerst wrote: post #13

Earlier quoted context omitted.

As 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.

Uh, 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...

A 980 Ti doesn't have FP16 hardware. The only Maxwell based component with such support is their Tegra part.
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