Faster folding

Graphic processors are contributing over one petaflop of processing power to Stanford University’s Folding @ home distributed computing application.


Graphic processors made by US chip house Nvidia are contributing over one petaflop of processing power to Stanford University’s Folding @ home distributed computing application, according to statistics published by the university.


Active Nvidia graphic processors deliver over 1.25 petaflops, or 42 per cent of the total processing power of the application which seeks to understand how proteins affect the human body.


Interestingly, Nvidia petaflop contribution, nearly half of the processing power on Folding @ home, is delivered by just 11,370 of the total active processors used in the project. In comparison, 208,268 central processing units running Windows contributed just 198 teraflops – just six per cent of the total processing power in the project.


Stanford University released a Folding @ home client specifically for Nvidia graphics devices in June, so the staggering advance has been achieved in only a few months.


Developed using Nvidia Cuda, a C language programming environment for many-core parallel architectures, the Cuda port of the Folding @ home client has delivered more processing power than any other architecture in the history of the project.


Vijay Pande, associate professor of chemistry, Stanford University and director of the Folding @ home project, said: ‘As these statistics show, the impact of Nvidia graphics processing units on protein folding simulations has been extraordinary. Teams that are folding with Nvidia graphics processing units are seeing huge boosts to their production and this is helping to accelerate the project significantly.’


Stanford University’s distributed computing program Folding @ home has become a major force in researching cures to life-threatening diseases such as cancer, cystic fibrosis, and Parkinson’s disease by combining the computing horsepower of millions of processors to simulate protein folding.


The Folding @ home project is the latest example in the expanding list of non-gaming applications for graphics processing units. By running the Folding @ home client on an Nvidia graphics processing unit, protein-folding simulations can be done 140 times faster than on some of today’s traditional central processing units.