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Petaflops? DARPA Seeks Quintillion-Flop Computers
coondoggie writes "Not known for taking the demure route, researchers at DARPA this week announced a program aimed at building computers that exceed current peta-scale computers to achieve the mind-altering speed of one quintillion (1,000,000,000,000,000,000) calculations per second. Dubbed extreme scale computing, such machines are needed, DARPA says, to 'meet the relentlessly increasing demands for greater performance, higher energy efficiency, ease of programmability, system dependability, and security.'"
From TFA, written by Michael Cooney and propagated by the summary:
It looks like these "extreme scale computing" systems are needed before things like "ease of programmability" can be acheived. I call bullshit.
The actual notice from DARPA is named Omnipresent High Performance Computing (OHPC). From the first paragraph of that page:
That makes a lot more sense.
Now, will someone please go and smack Michael Cooney up the back of head for writing like that?
Call me tinfoil hat wearer, but me thinks they want a faster way of cracking encryption...
Quintillion is not an SI prefix. The next step after Peta is Exa.
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They come up with ideas that only ultra-geeks and science fiction nerds could come up with, and then they get billions in funding for it! It's like paradise. The fact that they're actually successful at advancing human technology is just icing on the cake.
Simulating exploding hydrogen bombs, weather simulation, brute-force cracking, etc. Basically any distributed project you can think of (see BOINC) can also be done with a supercomputer.
It's a scientific model with a boatload of variables and dependencies. Ask these guys.
Well, what is it about weather simulation that requires so much work?
The enormous number of variables, mostly. Weather, nuclear bombs, ocean currents, cryptography, even things as seemingly simple as modeling air flow around an object. If you are looking to develop a model of a process that involves a few thousand variables and you need to know the interaction of those variables several levels deep....you need to make a lot of calculations.
It hasn't been all that long that computers have had the computational power to dominate humans in games as 'simple' as chess.
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There are broad classes of algorithms where you can make good use of essentially arbitrary amounts of computing power to get better answers. When doing physical simulations of something like airflow over a jet wing, or the movement of a weather system, or the explosion of a hydrogen bomb, you'll break everything up into tiny units that you treat as monolithic elements whose behavior can be treated relatively simply, and calculate what happens to them over some tiny timescale, call the result the new state of the universe, and repeat. This is called "finite element analysis".
Because you're calculating everything in discreet steps, though, errors creep in and accumulate. The more processing power you have, the more elements you can use and the smaller time scales you can calculate over and get a more accurate answer in the same amount of time. The reason it's unacceptable to do the same calculation but have it go 1,000 or 1,000,000 times slower is that these simulations might already take hours, days, weeks, or even longer. Even the longest DoD contract needs an answer to the behavior of a proposed jet fighter wing in less than 1,000,000 days. :)
Scientific computing is an area where there will always be a use for more processing power.
There are other areas where it can be important, when you have real time constraints and can't just reduce your accuracy to make it work. I recall a story from advanced algorithms class where a bank was handling so many transactions per day that the time it took to process them all was more than 24 hours. Obviously this was a problem. The solution in that case was to modify the algorithm, but that's not always possible, and you need more computing. This is a little different in that you need the extra power to allow growth, as opposed to science where you could hand them an exaflop computer today and they'd be able to use it to its fullest.
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In fluid dynamics simulations (which include weather stuff), there are huge computational problems. I work in the field, so bear with me a little.
The best model we have so far for fluids is to use balance equations (look up the Navier Stokes equations). This means that in order to describe the evolution of a fluid in a given domain, we need to split the domain into small cells, and then integrate numerically the balance equations. To put it simply, you have to integrate numerically a system of ordinary differential equations with many many variables (degrees of freedom).
For a simple but "correct" Navier Stokes simulation, the number of degrees of freedom is proportional to Re^(9/4), where Re is the Reynolds number (the memory requirements are proportional to the number of degrees of freedom). This Reynolds number, for typical systems (like the atmosphere) is of the order of at least 10^4-10^6 (you can look up typical values on wikipedia if you're interested). Furthermore, the number of timesteps needed for a "correct" simulation is proportional to Re^(3/4).
But these are not the most complicated simulations that are to be run on such machines. Research for issues like controled nuclear fusion needs to address much more demanding problems.
Numerical simulations of physical systems are inherently hard, because they scale polynomially with their complexity. However, they are generally cheaper than actual experiments, and you have access to more data.
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