Knowing How to Play the Chemical Analysis Game
Stan Gibson | Date: 07-21-09 | Comments: 0
- Powerful graphics processors score big in scientific computing.
Playtime’s over. The graphics processors that produce
life-like effects for the latest video games have been harnessed for the
serious work of chemical analysis. By rewriting algorithms originally crafted
for conventional CPUs to run on the graphics processors (GPUs) of video game
consoles, researchers at Stanford University and the University of Illinois
have sped up processing by 650 times.
GPUs are
suited to the task of scientific computation because of inherent parallelism of
their architecture. For example, the Nvidia chips used in this work each have
240 computing cores. But putting each core to work simultaneously on the task
of analyzing molecular structure was not easy.
“GPUs have
a large number of cores, but they are simple, so you have to find out how you
can program them. It took several months and a lot of experimentation to
rewrite the software. There is still not a lot known about the tricks for doing
this,” said Professor Thom H. Dunning Jr. at the National
Center for Supercomputing
Applications (NCSA) at the University
of Illinois in Urbana,
Ill., and lead chemist of the project.
Todd
Martínez, a professor of chemistry at Stanford
University and Stanford graduate
student Ivan S. Ufimtsev carried out much of the work on the project, rewriting
the molecular design program called GAMESS to calculate the structures
of molecules ranging from the 24-atom caffeine molecule to the 453-atom
olestra molecule.
A catalyst that
enabled the work was Nvidia’s release of Cuda (Compute Unified Device Architecture),
an extension to the C programming language that lets the CPU and GPU
work together on compute-intensive tasks. The trial ran on three Nvidia GeForce
GTX 280 GPUs, but the aim is to run on many more, piling parallelism upon
parallelism for far higher performance, said Dunning.
With the
chemical analysis success on the books, many other tasks can be tackled far
more efficiently than is now possible. Mathematical libraries, for example,
will be adapted to run on GPUs. “This is going to become much more common in
the future than it is now,” said Dunning.
Funding for
this research is provided by the NSF Divisions of Chemistry
and Materials
Research.
