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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.'"
Call me tinfoil hat wearer, but me thinks they want a faster way of cracking encryption...
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.
I mean, specifically, what can you do on something that fast that you couldn't do on one 1,000 (or 1,000,000) times slower? What kind of tasks need that much processing power? For example, you normally hear about them being used for things like weather simulation. Well, what is it about weather simulation that requires so much work?
Theoretically there's nothing you can't do on a supercomputer that you couldn't do with an ordinary desktop computer (except possibly for memory constraints), but for that matter you could also do everything by hand. The thing is, when your problem space is very large (i.e. calculating all interactions between X number of objects, where X is some huge number, or solving something like the Traveling Salesman Problem), you are limited in your options of what you can do to get results faster. If you're lucky, you can find some speedup of your problem (I.E. going to a better level of O-complexity [O(2^N)->O(n^2) would be a huge speedup, but doesn't happen often]), or tossing more resources at it. Yes, it'll still be slow, but if it takes you a year to do on a supercomputer, that's quite a bit better than spending 1000 years waiting on a regular computer.
If you take weather simulation :
At a given point, you have a bunch of physical equations taking a set of parameters at time t and giving you these same parameters at time t+1. Of course, the smaller the time step, the better the result.
To have the best possible result, you should consider the whole globe at once (think phenomenon like thermohaline circulation for example). However, you should also consider the finest grid possible, to take into account the heterogeneity of the geography, the local variations due to rivers, etc. It is also important to consider a three-dimensional model if you want to transcribe the atmospheric circulation, the evaporation, etc.
I forgot the exact numbers, but Wikipedia gives an example of a current global climate models using a grid of 500,000 points (see http://en.wikipedia.org/wiki/Global_climate_model ), which is a pretty coarse resolution, working with tiles of tens of thousands kilometer square.
With the current computing capabilities, we can not go much farther for a global model. This is already an impressive improvement compared the first models, which were two dimensional and used very simplified equations, overlooking a large number of important physical mechanism.
At the same time, we have satellite data several orders of magnitude more precise. Data from the satellite ASTER were computed to provide a complete altitude mapping of the globe with a theoretical resolution of 90 m. The vegetation cover can be obtained at a resolution of 8m using commercial satellite like FORMOSAT-2. Even the soil moisture can be measured at a resolution of around 50 km thanks to the new satellite SMOS.
These sets of data are already used at the local level, for example to model the transfer between the soil and the atmosphere, taking into account the vegetation (SVAT modelling). It makes no doubt that a global climate model using a more precise grid and these data would significantly improve its prediction.