* Astronomy

The Scout programming language, developed at Los Alamos National Laboratory (LANL) in California, US, enables scientists to run complex calculations on a computer's graphics processing unit (GPU) instead of its central processing unit (CPU), and see the results rendered almost instantly on their screens has been released by US researchers.

In tests, the graphics processor was able to perform certain types of calculation 12 times faster than a single CPU.
Graphics processors generate smooth and realistic three-dimensional imagery by performing rapid calculations on visual data. And the latest graphics chips rival CPUs for raw processing power, thanks to consumer demand for hardware powerful enough to support the latest 3D computer games.

"These chips normally sit idle when scientists work. They have all this processing power but it's just not being used." - Patrick McCormick, LANL researcher.

Researchers could use the Scout programming language to simulate various phenomena, such as ocean currents and the formation of galaxies. Performing these calculations on a graphics processor makes it simple to render simulations visually at the same time.
Researchers at LANL have already tested Scout by modelling a critical moment during a particularly spectacular astronomical event: a “core-collapse supernova”. The simulations ran 12 times faster than they do on a single CPU, primarily because the problem is so well suited to a graphics processors' capabilities.
The researchers simulated the shockwave produced after the core of a super-giant star collapses upon itself. The collapse occurs when a gravitationally unstable iron core has been generated by fusion reactions inside the star.

To make the technology much more powerful, McCormick is working on a version of Scout that will work when several computers are linked together.

http://libsh.org/download.html

"There is a real market driving this hardware that we can use for scientific computation" - Peter Schröder, a computer simulation expert at the California Institute of Technology.

This approach is particularly well suited to "anything that has high floating-point needs with low communication needs" – in other words intensive mathematical calculations that can be easily split up into individual portions. This is because graphics chips contain many individual processing cores that are suited to performing intensive calculations on their own.

But some experts say graphics chips’ design means they will not perform as well as CPUs on less specialised tasks.
"For general-purpose scientific computing, GPUs have not proven themselves yet" - Jack Dongarra, supercomputing expert at the University of Tennessee, US.


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