The sight of supercomputers in every home and office may soon become a reality thanks to video games such as Grand Theft Auto. High-end 3D games need the fastest graphics chips to run well. This has driven graphics cards makers to build ever-faster cards, and performance from the graphics processor on these cards is hundreds of times faster than the processor in a standard PC.
When people think of a supercomputer, they imagine boxes like the Cray - machines engineered with cutting-edge science to perform computational tasks as fast as possble. While a PC may take wekks, months, even years to run a task, Supercomputers use their vast processing power to perform calculations in minutes.
This means that for around £3,000 scientists will soon be able to buy supercomputers for their desks. Experts say within three years businesses and home users will be able to do the same.
Graphics chips are being adapted to share the work of the central processing unit to deliver supercomputing power. This allows a single PC to run tasks far more quickly than the combined computing power of a group of PC servers.
The development of sophisticated graphics chips and software development tools for vastly parallel programming, the same cards that power video games, has allowed this breakthrough in computing performance. Nividia has introduced CUDA ("Compute Unified Device Architecture"), a C-Compiler and set of development tools that allow programmers to use the C programming language to code algorithms for execution on Nvidia graphics processing units (GPUs).
Kees Joost BatenBurg, a researcher of the University of Antwerp, is using graphics chips to build a supercomputer which creates 3D models of internal organs.
"Modelling has a major disadvantage: the construction time. Even on our small cluster of four quad core PCs (16 processors) construction of a 3D model can take days. Not good if you're trying to perform a diagnosis." Rather than buy more PCs to speed up the cluster, BatenBurg has used the processing power of a graphics card from Nvidia to accelerate rendering time.
The university has built a supercomputer called Fastra to perform large-scale scientific computations. Fastra contains four Nvidia 9800GX2 graphics cards, that each contain two Graphics Processing Units (GPUs), giving a total of eigth graphics processors. By using these eight GPUs in parallel, the lab can obtain supercomputer performance - equal to a cluster of hundreds of PCs within a single PC, according to BatenBurg.
The Nvidia 9800GX2 graphics processing unit uses more than 128 smaller processors that can work in parallel with the main CPU processor, which usually has between two and four processors. This can make your PC 40 times faster, said BatenBurg.
"Having eight graphics processors work in parallel allows this system to perform as fast as 350 modern CPU cores for our tomography tasks, reducing the reconstruction times from several weeks (on a normal PC) to hours," he said.
Fastra is made completely from consumer hardware, the same type of cards computer enthusiasts buy off-the-shelf from PC retailers. It is built from four Nvidia 9800GX2 graphics cards - more than 1,000 small processors working together. The hardware has cost the university less than E4,000. This replaces a 512 processor cluster machine which would have originally cost E3.5m.
As Fastra is not a general purpose computer, its power heavily depends on the particular application it is used for. The research group has focused on the 3D tomography computations. For these computations, which can easily take weeks on a normal PC, Fastra performs as fast as more than 300 Intel CPU cores (Core Duo, running at 2.4GHz), giving the results in under an hour.
"Our local supercomputer cluster consisting of 512 Opteron cores, which cost millions of euros when constructed in 2005, is actually outpaced by Fastra in some cases," said BatenBurg.
"GPU computing - doing general computations using graphics hardware - is a very powerful technique," said. "We believe with Fastra, we have created the world's fastest computer to perform our calculations at the least cost," said BatenBurg.
The biggest problem was to find both a motherboard and a case able to store four 9800GX2s.
"This turned out to be a bit more problematic than previously anticipated. Most "normal" large cases only offer up to seven expansion slots." said Batenburg. Since every GX2 requires two of those, the lab would have been one short. The search eventually ended in Taiwan with a case from LIAN-LI. The motherboard was required to have at least 4 PCI-Express expansion slots, but also in such a way as to allow every graphics card to occupy double slot spacing. No SLI-supporting motherboard could offer us this.
The researchers selected Windows XP-64 as the operating system for Fastra. "There were two reasons for choosing this platform: first, we needed a 64-bit operating system, in order to utilize 8GB of RAM. Second, we expected fewer driver issues on Windows compared to Linux."
For development, the researchers used Microsoft Visual Studio 2008. The core functionality for the CPU code is written in C++ (Visual C++), while Matlab is often used as a front-end for rapid prototyping. All GPU code is developed using the Nvidia CUDA framework, a C-like programming language that allows for efficient programming of the NVIDIA GPUs.
This was first published in July 2008