Slashdot Mirror
Supercomputer Built With 8 GPUs
FnH writes "Researchers at the University of Antwerp in Belgium have created a new supercomputer with standard gaming hardware. The system uses four NVIDIA GeForce 9800 GX2 graphics cards, costs less than €4,000 to build, and delivers roughly the same performance as a supercomputer cluster consisting of hundreds of PCs. This new system is used by the ASTRA research group, part of the Vision Lab of the University of Antwerp, to develop new computational methods for tomography. The guys explain the eight NVIDIA GPUs deliver the same performance for their work as more than 300 Intel Core 2 Duo 2.4GHz processors. On a normal desktop PC their tomography tasks would take several weeks but on this NVIDIA-based supercomputer it only takes a couple of hours. The NVIDIA graphics cards do the job very efficiently and consume a lot less power than a supercomputer cluster."
I am guessing it has something to do with floating point calculations vs. integer calculations, but if I read the article, this wouldn't be Slashdot, would it? Think about it. We have GPUs to perform vector maths, flops, etc. because the CPU is not all that great at that sort of thing typically. A general purpose CPU is not necessarily going to be the fastest if your problem domain is more suited to an "inferior" chip; general purpose CPUs are not designed to be the fastest chip in every situation.
By the benchmark that they solve the particular problem of this specific application in 1/300th of the time?
Help stamp out iliturcy.
I can't imagine that it is a coincidence that this comes along just as Nvidia are crowing about CUDA, or that the resulting machine looks like a gamer's dream rig.
While there is ample crossover between hardware enthusiasts and academia, anyone soley with the computation interest in mind probabyl wouldn't be selecting neon fans, aftermarket coolers or spend that much time on presentable wiring.
They are useful for applications that can be massively parallelized. Your average program can't break off into 128 threads, that takes a little bit of extra skill on the coder's part. If, for example, someone could port gcc to run on the GPU, think of how happy those Gentoo folks would be :) (make -j128)!
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this is an example of acceleration architecture. Anyone who have used FPGAs knows that. Ofcourse, making sensational news is a too common thing on /.
They called me mad, and I called them mad, and damn them, they outvoted me. -Nathaniel Lee
Pardon the italics, but I was impacted by the killer slant of this posting.
For specific kinds of calculations, sure, GPGPU supercomputing is superior. I would question what software optimization they had applied to the 300 CPU system. Apparently, none. Let's not sensationalize quite so much, shall we?Invenio via vel creo
A Tesla system would cost a lot more.
What they are is doing is reconstruction, basically analyzing the raw data data from a tomographic scanner and generating a representation which can then be visualized. So its more doing numerical methods than graphics.
And BTW even rendering the reconstructed results is not that simple, as current graphics card are optimized for geometry, not volumetric data.
I think the GP (and myself) were objecting to the use of the fairly general word "power" and the use of this one problem as a "power benchmark". While it is obviously true that 8GPUs is as fast as 300 C2Ds for this problem, this system isn't as fast as a supercomputer for most problems. All this does is point out that the recent trend of building supercomputers out of inexpensive general purpose CPUs may not be a good idea for all applications.
When you get into inverting matricies, or doing matrix vector multiplication the algo is very easily in parallel, but I always wonder where the full matrices live. i.e. they could easily be tens of GBs of matrix, so the CPU would seem to have to be heavily involved as well.
Because for 95%+ of the problems a general purpose computer tackles GPU's would suck. It's only in very special cases that GPU's outperform CPU's. Thus, your idea is a poor one.
As far as I know, GPUs are amazingly fast at matrix operations and other things allowing vectorized evaluation. I guess these tomography applications must make massive use of these. After all, tomography is in essence image processing..
The state you are in while your HEAD is detached... - wait, what?
And... a screwdriver is not always a prybar. A tool's a tool - they have preferred usage but if your requirement is specific and you're creative enough, you can do some fine work outside of the tool's intended purpose. Like this guy. Kudos to him.
Perhaps some more creative people finding this information will now discover if their specific requirements can be met by this interesting configuration. That will save them large quantities of cash or possibly enable some facility that was not previously available because supercomputers cost a grip-o-cash.
Of course for general purpose supercomputing you would want to use modified PS3s.
Help stamp out iliturcy.
Precisely. But that happens to be one of the areas where more performance is still needed.
You don't need a super-duper CPU for text editing, that's for sure. For most of the tasks people do on computers, we have had CPU enough for the last 15 years or more. But where we still need more CPU happens to be mostly in tasks that ARE massively parallel, for instance, physics simulations, of which you will find several examples in the nVidia site.
I'm following this technology with much interest, and I think I will have a major upgrade in my home computer soon. My old FX-5200 card has been more than enough for my gaming needs, but now I have a new reason for upgrading.
20th century thinking. Welcome to globalization. The product was designed, manufactured, and purchased on Earth.
"Convictions are more dangerous enemies of truth than lies."
Please, please, please do the math.
8 GPUs are being compared to 300 CPUs. So the single GPU for this pupose isn't 300 times as powerful as the CPU.
It is doing the operation in 1/37th the time approximately. This isn't news or unbelievable. GPUs are dedicated to performing certainly types of tasks far better than a CPU.
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Unpredictably.
(the big shift over the last 6 years is mostly due to wanton printing of money in the US and rather tight central banking in Europe [with a healthy dose of Chinese currency rate fixing thrown in]. The trend isn't all that likely to continue, as a weakening dollar is great for American businesses operating in Europe and horrible for European businesses operating in America, which creates [increasing amounts of] counter-pressure to the relatively loose government policy in the US, or saying it the other way around, counter-pressure to the relatively tight government policy in the EU.)
Nerd rage is the funniest rage.
Sure - but at 4000 euros, you can afford to do a one-off purchase and write custom software for a limited application. The point of this is that if your application suits it, this is a very cheap way to get supercomputer performance without paying for your own supercomputer (cluster) or time on an existing one.