On Mon, 6 Dec 1999, John Clark wrote:
> however today IBM announced they will spend $100,000,000 make a machine of
> that power in just 5 years. This will be called "Blue Gene" and will be
> specially designed to do one thing, not play chess but solve the awesomely
> complex protein folding problem.
Waaallll, just stomp my face right into the mud... I just posted a note yesterday to the EI List commenting on the discussion of this in the "Blitzing Bits" (pg 57) article in the Scientific American Extreme Engineering special issue with my interpretation that IBM had not made a commitment to this --
"This is a cheap way to build supercomputers if you need them for just one kind of problem, says "Mark Snir, manager of scaleable parallel systems at IBM. 'We looked here at what it would take to build a multipetaflops machine customised to the problem of protein folding," he says. "We could do it for a few million dollars--much, much less than a general-purpose machine.'"
and here I find that the next day, they are making a commitment (presumably after the marketing guys and managers had inflated the cost a bit).
> IBM thinks there is lots of money to be made in analyzing the huge
> amount of data that will come from the Human Genome Project,
They got that right.
> I think it could also be used to design nano machines.
Phooey... You could design nanomachines now. After all Eric & Ralph have done specific parts and NASA Ames has simulated actually running them.
The protein folding problem is *much* more difficult than nanoscale design because of the large numbers of degrees of freedom you have in protein folding. For most nanoscale designs I can envision (e.g. scaling down macromachines or MEMS machines to nanoscale) you absolutely do not want that. The design space humans work in has many fewer degress of freedom because our minds can't deal with anything too complex. Nature on the other hand wants many degrees of freedom because they are required to successfully explore the phase space. [In nature most designs don't work, so you have to try a lot of them to find the ones that do work.]
Now the machine will be useful for two things (a) really good drug design, because you will be able to model protein flexing much more effectively and develop drugs that bind much better, or drugs that bind well to one specific receptor and not 4 other versions of a receptor. And (b) the de novo design of enzymes, in the event that hard/dry nanoassembly proves very difficult and we have to go the biotech route to pseudo-hard nanotech.
> To achieve these blistering speeds a radically new computer architecture
> must be used that has 1,048,576 processors. IBM will develop a new chip
> that contains 32 processors as well as lots of on chip memory but has
> only 57 machine level instructions compared with about 200 for most RISC
Yes!!! They are going to processor in memory (PIM). You AI folks should be wetting your pants with excitment. Think about it from the architecture standpoint -- this means they can have a slightly different architecture the processors only have a few instructions but effectively function as neurons. By going to PIM, they are solving the interprocessor bandwidth problem which is the *real* bottleneck to getting brain-equivalent computers.
> A great deal of effort will also go into making an operating
> system that is more fault tolerant than anything now in existence, if one of the
> processors or even an entire chip malfunctions the supercomputer will not die,
> it will just slow down very slightly.
As is true in the brain as well...
My calculations for the PIM approach give us brains on a desk sometime between 2007 and 2015. Figuring 32 processors/chip, 1 million processors works out to 32K chips, figuring 16 chips per board works out to 2048 boards. It isn't going to fit on my desk but it will fit in a couple of racks. For a first generation it isn't bad.
If they price this at $500/chip (processor costs), that works out to $16 Million (big pharma & government only), but if they drop the price down to $25/chip (current memory costs), that works out to $819K, still a little expensive but universities and even small companies could afford one. If the price falls by another order of magnitude ($80K; Korean or Taiwan fabs offer knock off designs perhaps), then we are talking a range where the transhumanist collective could buy one. If it comes down by two orders of magnitude ($8K) a lot of companies will be working very very hard to replace wet brains with dry brains.
It will be interesting to see what the power requirements are (after all its mostly memory right?). Figuring 1W per chip (bet thats low), that means I need 32K W, which at 120V works out to 273A. Damn, that exceeds my current home electrical service, gotta call the electrical contractor now. While I'm at it I better get the A/C guy too.
If anyone gets URLs with more technical details, please send them to me.