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."
As far as my understanding goes, comparing a GPU's performance to a CPU's performance is very very task dependent and the comparison with 300 CPUs should not be taken to mean that a 8GPU system is more powerful than a 300 core duo system in general.
If the application requires solving a small task many times over and over and all of these tasks can be done in parallel then using a GPU works great because a GPU has many cores each of which can handle a simple routine. Also the GPU is designed to spend very little time on the way code is hadled (load, switch etc) and spend more time actually running the code (hence the requirement of only very simple functions).
Such problems frequently arise in tomography, physics, astronomy etc and I hear GPUs are a great success in these areas. But don't hold your breath for running your favorite distro blazingly fast using GPUs.
Wave of the Future? Yes*. Revolution in computing? Not quite.
The GPGPU scheme is, after all, a re-invention of the vector processing of old. Vector processors died out, however, because there were too few users to support. Now that there's a commercially viable reason to make these processors (PS3 and video games), they are interesting again.
The researchers took a specialized piece of hardware, rewrote their code for it, and found it was faster than their original code on generic hardware. The problems here are that you have to rewrite your code (High Energy Physics codebases are about a GB, compiled... other sciences are similar) and you have to have a problem which will run well on this scheme. Have a discrete problem? Too bad. Have a gigantic, tightly coupled problem which requires lots of inter-GPU communication? Too bad.
Have a tomography problem which requires only 1GB of RAM? Here you go...
The standard supercomputer isn't going away for a long, long time. Now, as before, a one-size-fits-all approach is silly. You'll start to see sites complement their clusters and large-SMP machines with GPU power as scientists start to understand and take advantage of them. Just remember, there are 10-20 years of legacy code which will need to be ported... it's going to be a slow process.
nVidia's CUDA framework for performing general purpose operations on a GPU is something totally different. I don't think the Amiga custom chips could be repurposed in such a fashion.