NVIDIA Unveils 2 Petaflop DGX-2 AI Supercomputer With 32GB Tesla V100, NVSwitch Tech

bigwophh writes from a report via HotHardware: NVIDIA CEO Jensen Huang took to the stage at GTC today to unveil a number of GPU-powered innovations for machine learning, including a new AI supercomputer and an updated version of the company's powerful Tesla V100 GPU that now sports a hefty 32GB of on-board HBM2 memory. A follow-on to last year's DGX-1 AI supercomputer, the new NVIDIA DGX-2 can be equipped with double the number of Tesla V100 processing modules for double the GPU horsepower. The DGX-2 can also have four times the available memory space, thanks to the updated Tesla V100's larger 32GB of memory. NVIDIA's new NVSwitch technology is a fully crossbar GPU interconnect fabric that allows NVIDIA's platform to scale to up to 16 GPUs and utilize their memory space contiguously, where the previous DGX-1 NVIDIA platform was limited to 8 total GPU complexes and associated memory. NVIDIA claims NVSwitch is five times faster than the fastest PCI Express switch and offers an aggregate 2.4TB per second of bandwidth. A new Quadro card was also announced. Called the Quadro GV100, it too is being powered by Volta. The Quadro GV100 packs 32GB of memory and supports NVIDIA's recently announced RTX real-time ray tracing technology.

Comments

This Is Not A 2 Petaflop Supercomputer At All

[dryriver]

The Nvidia V100 is a 15 TeraFlops capable GPU at 32 Bit accuracy, and half that at 64 Bit accuracy. You'd need a whopping 134 of these GPUs in a box with perfect parallelization between them to hit 2 TeraFlops for general GPGPU compute tasks. Nvidia claims that the TENSOR cores in a V100 deliver about 120 TeraFlops of MACHINE LEARNING performance. How they measured this is an open question - did they take a machine learning task that was 120 times faster than a 1 TeraFlop CPU with no AI optimization could do, and magically arrive at 120 TFLOPS? What AI tasks these TENSOR core TeraFlops can be used for is the next question. So for anyone thinking "I can get 2000 GPGPU TeraFlops in 1 box", sorry that isn't the case here. For specific AI tasks, this may be the machine to get. For general GPGPU, this thing is just a casing with a couple of 15 TFLOP GPUs crammed together.

Re: This Is Not A 2 Petaflop Supercomputer At All

[Anonymous Coward]

A flop is a flop, no matter how many petas.

Re:This Is Not A 2 Petaflop Supercomputer At All

[Anonymous Coward]

The Nvidia V100 is a 15 TeraFlops capable GPU at 32 Bit accuracy, and half that at 64 Bit accuracy. You'd need a whopping 134 of these GPUs in a box with perfect parallelization between them to hit 2 TeraFlops for general GPGPU compute tasks. Nvidia claims that the TENSOR cores in a V100 deliver about 120 TeraFlops of MACHINE LEARNING performance. How they measured this is an open question - did they take a machine learning task that was 120 times faster than a 1 TeraFlop CPU with no AI optimization could do, and magically arrive at 120 TFLOPS? What AI tasks these TENSOR core TeraFlops can be used for is the next question. So for anyone thinking "I can get 2000 GPGPU TeraFlops in 1 box", sorry that isn't the case here. For specific AI tasks, this may be the machine to get. For general GPGPU, this thing is just a casing with a couple of 15 TFLOP GPUs crammed together.

It is literally called an __AI__ supercomputer, the target market and intended purpose is deep learning, training, and inference. Tasks which make use of the tensorcores which are matrix-multiply-and-accumulate units.
Sure the flop count is only on workloads making use of the tensorcores, but seeing as how that's the market for it anyway I see no problem.

2 Petaflop?

[cold fjord]

I thought that nobody needed more than 640 teraflops?

Re:Fuck raytracing

[mark-t]

What difference does that make? You'll spend more on electricity trying to mine them than what bitcoin is worth.... it's been that way for years.

Re:Fuck raytracing

[dryriver]

Its only the TENSOR cores for Machine Learning and AI tasks that supposedly deliver 120 TFlops per GPU card. The card itself does just 15 TFlops for general computation tasks. So unless you can figure out how to mine Bitcoin using the Tensor cores, these Volta V100 GPUs are basically just like the GTX 1080 GPU, just with about 5000 CUDA cores and more RAM capacity.

Re:But will it run Crysis?

[dryriver]

It can run Crysis BACKWARDS and SIDEWAYS at 1,000,000 FPS. The gameplay is also far more tense, because you are using Nvidia's new TENSOR cores.

Re:Fuck raytracing

[InvalidsYnc]

Yes, Please, let those stupid fricking bitcoin miners start using something other than the top end of the consumer line so that regular people don't have to spend so damn much or wait so damn long to buy a decent video card.

Now, what I really meant:

Bitcoin miners should stop buying the fucking graphics cards all up so that I can buy one cheap. Assholes.

But can it

[AHuxley]

Ray trace at 8K?

RoboCop

[harvey the nerd]

is that the new enchanced version with the Robo-Cop routines to whack old homeless ladies pushing their cart or bicycle across the street....

Re:RoboCop

[bennet42]

No that's the Death Race 2000 version of AI that accidentally got loaded by Uber.

Re:But will it run Crysis?

[PopeRatzo]

It can run Crysis BACKWARDS and SIDEWAYS at 1,000,000 FPS. The gameplay is also far more tense, because you are using Nvidia's new TENSOR cores.

I just had a brainstorm. There should be a version of Crysis that mines bitcoins while you play and the more dudes you kill in the game the more bitcoins it mines.

That's totally my idea don't none a you try to steal it I'm going to patent it in the morning. Or copyright it. I can't remember which.

Imagine . . .

[Joey Vegetables]

Imagine a Beowulf cluster of these!

Yeah, I'm showing my age. So what?

Re:Imagine . . .

[LoganTeamX]

I laughed, well played. Also showing my age here.

Re: Fuck raytracing

[Anonymous Coward]

AMD fan, I take it? Your point is valid, but you keep harping on it as if it's some big conspiracy you're uncovering...

But . . .

[hduff]

Can it play DOOM?