Slashdot Mirror
SGI & NASA Build World's Fastest Supercomputer
GarethSwan writes "SGI and NASA have just rolled-out the new world number one fastest supercomputer. Its performance test (LINPACK) result of 42.7 teraflops easily outclasses the previous mark set by Japan's Earth Simulator of 35.86 teraflops AND that set by IBM's new BlueGene/L experiment of 36.01 teraflops. What's even more awesome is that each of the 20 512-processor systems run a single Linux image, AND Columbia was installed in only 15 weeks. Imagine having your own 20-machine cluster?"
...when they hit the "TURBO" button on the front of the boxes they'll really scream.
I have one of those... in a spare room!
Who cares about a 20 system cluster, I want a one 512 processor machine!
or 20, I'm not that picky
Just what I need to model my next H-bom... uhh... umm.... I mean render my next feature film. I call it "Kaboom."
I bet gentoo wouldn't be such a b**ch to get running with all of that compiling power behind it :)
If the same software is used, its not going to make weather predictions more accurate. Its just going to give them the wrong answer, faster.
This page contains images of the NASA Altix system. After reading the article I was curious as to how much room 10K or so processors take up.
http://www.busyweather.com/
Tomorrow looks like developing a slight rise in Insightful post, but a drop in overall Informative. "First Post" will remain as a constant pattern.
Wow, I didn't know the NewAdvancedSearchAgent had such an interest or budget for super computing. I'd think they'd be able to afford their own web server though instead of being parked at domainspa.com and having to fill their entire page with advertisments.
Try NASA.GOV.
Why does it take so long to build a super computer and why do they seem to be redesigned each time a new one is desired?
It's a little like how Canada's and France's nuclear power plant system are built around standardized power stations, cookie cutter if you will. The cost to reproduce a power plant is negligble compared to the initial design and implementation, so the reuse of designs makes the whole system really cheap. The drawback is that it stagnates the technology and the newest plants may not get the newest and best technology. Contrast this with the American system of designing each power plant with the latest and greatest technology. You get really great plants each time, of course, but the cost is astronomical and uneconomical.
So to, it seems with supercomputers. We never hear about how these things are thrown into mass production, only about how the latest one gets 10 more teraflops than the last and all the slashbots wonder how well Doom 3 runs on it or whether Longhorn will run at all in such an underpowered machine.
But each design of a supercomputer is a massive success of engineering skill. How much cheaper would it become if instead of redesigning the machines each time someone wants to feel more manly than the current speed champion, that the current design be rebuilt for a generation (in computer years)?
There's also a dark horse in the supercomputer race; a cluster of low-end IBM servers using PPC970 chips that is in between the BlueGene/L prototype and the Earth Simulator. That pushes the last Alpha machine off the top 5 list, and gives Itanium and PowerPC each two spots in the top 5. It's amazing to see the Earth Simulator's dominance broken so thoroughly. After so long on top, in one list it goes from first to fourth, and it will drop at least two more spots in 2005.
Whoever corrects a mocker invites insult;
whoever rebukes a wicked man incurs abuse.
--Proverbs 9:7
Does anyone know how much this system cost? It would be interesting to see how good of a teraflop per million dollar ratio they achieved.
For example, I know the Virginia Tech cluster (1,100 Apple Xserve G5 dual 2.3Ghz boxes) cost just under $6 million, runs at a bit over 12 teraflops, so it gets a bit over 2 teraflops per million dollars.
Other high-ranking clusters would be interesting to evaluate in terms of teraflops per million dollars, if anyone knows any.
Seriously, am I on candid camera?
Seti@home is currently reporting 70.93 TeraFLOPs/sec. It would be Number One if the list were a bit more inclusive.
Ok, so we have Linux doing tens of teraflops in processing, FreeBSD doing tens of petabits in networking,
It's a small world and it smells funny; I'd buy another if it wasn't for the money; Take back what I paid (SoM)
The answer here is "complexity". I do some scientific computing (have done chemistry, then materials science, now doing photonic devices) and there's always more you want to be able to consider. Of course, the best I've used is an 8-processor SGI machine (although that one was a bit old - I think the 2-processor opteron system I'm using now is actually better). But especially with the materials studies, ideally we wanted to do everything with full quantum-mechanical calculations. which turns into gigantic matrices, even for a system of 100 atoms or so. And even then we put strict limits on what orbitals we consider and all that good stuff.
Slightly more concrete example - right now with my photonics simulations (finite element) on my dual-opteron rig the max I can handle is about 180,000 elements (which means a (4*180000)x(4*180000) matrix with complex elements needs to be diagonalized, among other things), and it takes about half an hour for a standing-wave calculation. To do any time propogation, repeat same calculation in picosecond increments. And with the gridding I can do, for a 100 micron disc resonator in 2-D I have to use light at about 40 microns. To go to the 320nm wavelength these resonators are operating at, I'd need roughly 2 orders of magnitude more memory. There's also the time factor to be considered. As with any design process, one must iterate. Tweak a little here, run the program, rinse, repeat. How long are you willing to spend in this process before you feel something is "good enough"? The faster the computer spits the answer out, the more things you can try, and the more you can think things over and hopefully make it better.
And this is a single component in what can be a fairly complex integrated-photonics chip. [And might I mention again I've been working in 2-D this entire time instead of doing a full 3-D simulation?] You give me the computational power and I'll use it. And I'm an experimentalist doing fairly basic research who just wants to check some stuff in the computer before sinking a lot of time and effort into fabricating a test device.
On the other hand, I actually don't want to have one of the T100 supercomputers in our lab. That would mean I'd be spending all day writing code and designing complex simulations instead of in the lab getting my hands dirty.
And as for the commonality of problems requiring such computational power, I think almost any sort of simulation can easily use it. Consider more terms (everything I've done to date is horribly linearized - let's see some more terms in the Taylor expansion) to account for nonlinear behavior, grid things up finer to get more accurate results, consider more possibilities when dealing with chaotic behavior... I would hope any good scientist would find the possibilties endless.