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Two Directions for the Future of Supercomputing
aarondsouza writes: "The NY Times (registration required, mumble... mutter...) has this story on two different directions being taken in the supercomputing community. The Los Alamos labs have a couple of new toys. One built for raw numbercrunching speed, and the other for efficiency. The article has interesting numbers on the performance/price (price in the power consumption and maintenance sense) ratios for the two machines. As an aside... 'Deep Blue', 'Green Blade' ... wonder what Google Sets would think of that..."
Or better, give a userid and passwd for a NYTimes account.
I'm sure it's legal. It's like sharing/swapping discount passes at the supermarket.
DNA is the ultimate spaghetti code.
I think if you add up all the Watts sucked up by the myriads of smaller PC's in those projects, you'd get a respectable amount of electricity too... Imagine just the inefficiency of having monitor screens on all these machines sucking up power alone.
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Ok, Q is rated at 30 teraops at 5 MW. Green Destiny is capable of 160 gigaflops at 5 kW.
This means that the power efficiency difference is just a mere factor of 5. The problem with supercomputing is of course scaling and interconnecting the cpu... The author argues that the Green Destiny is "not so picky", and "hums away contentedly next to piles of cardboard boxes and computer parts" while Q requires special buildings and monstrous cooling installations. Yeah, so what, it is a much smaller machine.
Of course it is easier to build a smaller machine than a large machine. I would say that despite the fact that Green Destiny is 0.5% as fast as Q and is designed with power consumption in mind it is just 5 times as efficient.
Can anyone tell me (or point to a resource) how CPU power consumption depends on transistor size and clock frequency. Will a chip with a given size operating at a given clock frequency require the same amount of power, regardless of the number of transistors in it?
Heh. You forget that none of the causes you mention involve playing with huge computers. A lot of these machines are, however, being used to do protein-folding simulations- Blue Gene, or the PNNL's new machine (I think). I'm fine with simulating nukes, because it means fewer Pacific islands get slagged. Protein folding, on the other hand, is often something of a joke- some people get very interesting results that tell us a lot about biophysics, but absolutely nothing whatsoever indicates that we'll be able to do accurate structure prediction anytime soon. It's amazing how many people think completely computerized drug design is right around the corner.
From what I've read, a real useful advance in computational biology would be to automate building and refining of protein structures from crystallography. It's just not as sexy, though.