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Virginia Tech Supercomputer Up To 12.25 Teraflops
gonknet writes "According to CNET news and various other news outlets, the 1150-node Hokie supercomputer rebuilt with new 2.3 GHz Xserves now runs at 12.25 Teraflops. The computer, the fastest computer owned by an academic institution, should still be in the top 5 when the new rankings come out in November."
Reflecting on the comment: "hould still be in the top 5 when the new rankings come out in November." There seems to be a serious push for multiprosessor systems, currently the ranking seem to consist of a couple of stars, few big ones(this computer among them) and a huge group of third category, and then the "used to be great" computers. But from my reading of the trends seems that there will be more and more crowding at near the top, so I expect the second category to be much larger, with much smaller differences.
If that were feasible, you could be looking at toppling Earth Simulator at a fraction of the cost.
From the article:What I really want to know is what they do with the old machines. The articles speaks of the cluster being 'upgraded' - are the older G5s replaced, or do they just become part of the new cluster?
Still, I suppose there's one or two unwanted G5s - anyone want to send me a couple?
Tedious Bloggy Stuff - hooray?
Hans Moravec's book "Robot" suggests that 100 teraflops is about the level required for human intelligence. So we are up to 10% of his target. But human intelligence still seems very far away, so either he has badly underestimated, or our collective programming skills need significant improvement.
If you're referring to the old G5 Powermacs used in the original System X...they were sold. I bought one!
are not designed for the same type of work as clusters. If a probably is not effeciently parallizable and requires shared memory then a Cray is the only feasible option A Cray is not a cluster. It's like comparing mph for a sports car and truck: the car is faster but they are meant for different types of loads.
To be fair to the original poster, the Cray system he was referencing is a cluster system. Then again, its a cluster system with very impressive interconnects for which System X just isn't comparable (ie. The Cray system will scale far far better), not to mention the Cray software (UNICOS, CRMS, SFW), and the fact that the Cray system is an "out of the box" solution. So you are right, there is no comparison.
Jedidiah.
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I think Morevec's method of simulating human intelligence involves modelling a scanned copy of the human brain, in real time at a neuronal level. It would be similar to modelling the global weather system, a software capability we already have. Current neuroscience would expect this model to be functionally equivalent to a human mind in terms of matching inputs and outputs. As an aside, I know that Ray Kurzweil has I much higher required estimated of a 20 petaflop (20,000 teraflop) computer, based on more conservative assumptions. 20 petaflops is due around 2009/10 under Moore's law. (And I for one offer an early welcome to our expected new AI overlords ...)
It doesn't matter which ape activates the Monolith
I actually asked Hans a similar question at a talk he gave a while back, and he didn't really answer it, to my disappointment. My question was that "In nature the algorithm and computer were evolved together, so we'd expect them to be at a similar level of advancement. So, even if we get a computer as fast as a human, it might it not be nearly as smart since our programs do not use it efficiently enough?" In other words, Moore's law isn't helping us write better software (in some ways quite the contrary).
I'm a robotic software researcher, so this notion really affects me. IMO Software will lag well behind hardware, since it doesn't scale out nearly as well. Representation is of course a huge problem I won't even try to touch... But rest assured lots of people are working on all these things. Btw, It also doesn't help that CPU designs aren't even trying to make AI-style algorithms fast, but we can't blame manufacterers for that util there is demonstrable money to be made.
(Although I don't believe brain scanning quite hits the resolution mark required yet.)
It doesn't matter which ape activates the Monolith
I have a question from a casual observer who comes across this Hokie machine and the top 500 list every now and then. What is it these computers do?
Hearing it referenced in terms of AI helps, but is that the only purpose for a research facility to build one of these mammoths? Are there practical applications for the business world (other then the readily available (read commercial) clustered data warehousing)?
I'm not trolling, just curious.
Prof. Jack Dongarra of UTK is the keeper of the official list in the interim between the twice yearly Top 500 lists:
http://www.netlib.org/benchmark/performance.pdf (see page 54)
There have been some new entries, including IBM's BlueGene/L, at 36Tflops, finally displacing Japan's Earth Simulator, and a couple other new entries in the top 5.
Here's just the top 16 as of 10/25/04:
http://das.doit.wisc.edu/misc/top500.jpg
No matter what anyone says, Virginia Tech pulled an absolute coup when they appeared on the list at the end of 2003: no one will likely EVER be able to be #3 on the Top 500 list for a mere US$5.2M...even if the original cluster didn't perform much, or any, "real" work, the publicity and recognition that came of it was absolutely more than worth it.
Also interesting is that there is also a non-Apple PowerPC 970 entry in the top 10, using IBM's JS20 blades...
Actually, it's not quite that simple. As someone whose research is in modeling the hippocampal region CA3 (about 2.5 million neurons in humans, 250k neurons in rats), I can tell you that the connectivity of the system is a very important variable. And there is still much we don't know about the connectivity of the human brain. Furthermore, there are hundreds of different types of neurons in the human brain. Why so many different types if only 2 or 3 would do? Seems evolution took an inefficient path - unless, as is probably the case, the differences in the neuron types are crucial for the human computer to work the way it does. Granted, some differences might be due to speed or energy efficiencies which are not absolutely critical for early stages, but I suspect that many differences have to do with the software (or wetware in this case) that makes us intelligent.
After we've solved that minor problem, I think teaching the system will be relatively trivial. I.e., if we understand the wetware enough to reconstruct it, we most likely understand how its inputs relate to our inputs, etc., and we could teach it much the same as we teach a human child. Of course, we might also figure out a better way to teach it, and in so doing we might even find a better way to teach human children. (Some of our research has recreated certain known best learning strategies, it is probably only a matter of time before simulators disover a better one!)
Ben Hocking
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