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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?"
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.
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)
I don't have a square footage number, but it's the overwhelming majority of the server floor. We had to "clear the floor" earlier this summer to make room.
there are no stupid questions, but there are a lot of inquisitive idiots
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.