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Comment by phreeza | Hacker News Reader

A bit more informative is the actual fact sheet put out by the white house [1]. What they are really aiming for is exascale computing, which they define as being capable of applying exaFlops to exabytes. From my limited knowledge, the latter will actually be the bigger deal. As pointed out elsewhere, an exaflop supercomputer will probably come around beforehand.

[1] https://www.whitehouse.gov/sites/default/files/microsites/os...

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The real problem is getting 1 exaflop (or around it) within a reasonable power budget. The DOE's power budget for all of their supercomputing resources is 20 Megawatts, so at a full system level we would need to be at 50 GFLOPs per watt, while the best system right now is at 5.

> "I'd say they're targeting around 60 megawatts, I can't imagine they'll get below that," [Mark Parsons] commented. "That's at least £60m a year just on your electricity bill."

I wouldn't trust a Brit to report the figures for the US Department of Energy. The DOE's budget is 20 Megawatts [source: http://science.energy.gov/~/media/ascr/ascac/pdf/meetings/20...]

He didn't report it, he gave an estimate. And as for "wouldn't trust a Brit", I'd trust anyone directly involved in it, regardless of nationality.

Nvidia's Pascale GPU, due next year, is supposed to do 28 terraflops with 1300 watts which I make 21 GFLOPs per watt.

20+ GFLOPS/W for a single part isn't something new: http://streamcomputing.eu/blog/2012-08-27/processors-that-ca... (2012)

A huge amount of overall system power is spent in data transport. Plus, double everything for cooling. That brings the total system efficiency way down from what the actual computational components spend.

not 20 real (64b) gflops/watt. and no, not much power is used on interconnect, and certainly not a PUE of 2.0.

That's single precision, which the DOE doesn't care about. The latest NVIDIA GPU's do 8 to 16 times better at single precision (32 bit) floating point compared to double precision (64 bit).

The best next generation DP GFLOPs/watt from one of the big players will most likely be the 2016 Xeon Phi, at ~10-12GFLOPs/watt... You are also forgetting that GPUs also have a ~100W+ CPU sitting next to it, which brings down total efficiency significantly.

Shameless self promotion: My startup (http://rexcomputing.com) is aiming for 64 double precision GFLOPs/watt, and 128 GFLOPs/watts single precision for its first chip next year.

Your chip looks cool. I guess it may be tricky to adapt software to run on the thing? Or else you could try to sell Obama 4 million of them for his new computer.

Clearly designed to run AI for cyber-warfare / cyber-defense purposes? William Gibson's Black Ice coming to life?

More likely its for modeling nuclear weapons which I believe is the primary purpose of NSCI.

NNSA*, which is an office within the Department of Energy.

National Strategic Computing Initiative (NSCI) is the initiative within the DOE.

The story linked below about the French exascale plans explicitly states that it is the military applications division of the French nuclear energy agency (CEA) that is spearheading that effort. (I believe they work on both reactors and warheads.)

Serious question: what's left to model? They're already big enough to kill everyone, and we have heaps of them.

Often they model how the warhead ages, rather than how it explodes.

Yes, this is my understanding.

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