Frequently Asked Questions
What was the Petaflop Initiative?
Where is Folding@home now?
Why push for new technologies, such as the GPU, PS3, and the SMP clients?
How do the new clients improve FAH’s speed?
Since 2000, Folding@home (FAH) has made a major jump in the capabilities of molecular simulation. By joining together hundreds of thousands of PCs throughout the world, calculations which were previously considered impossible have now become routine. FAH has targeted the study of protein folding and protein folding disease, and numerous scientific advances have come from the project.
In 2006 the FAH team (with key collaborators in industry and at Stanford University) began a new initiative to take Folding@home to the petaflop scale. This is a 1,000,000,000,000,000 floating-point operations per second, a level of performance that was unmatched by even the fastest supercomputers. To reach this, we needed new technology and new computational methods. Streaming processors have become common in inexpensive consumer electronics such as the Cell processor found in Sony’s PlayStation 3 and the GPUs from ATI or NVidia found in the video processing sections of personal computers. Never before had either of these technologies been seriously utilized for distributed computing or molecular simulation. Our goal was to apply these new technologies to push Folding@home into a new level of capabilities. After much work, we released our first GPU client on October 2006, and our PS3 client the following March. Thanks to these platforms, six months later on September 16, 2007, Folding@home became the first distributed computing project to cross the petaflop barrier, and we were recognized by Guiness World Records. The PS3s and GPUs were so fast that we started looking at the some results several times a day instead of a couple times a month!
Thanks to your help and these new technologies, Folding@home is now steadily in the five and six petaflop range. In the years following our breakthrough in 2007, its remained the world’s most powerful computing system of any kind. We’ve been able to study very complex proteins and perform many drug design and viral entry simulations that would have been impractical before. This power has also allowed us to make our models more accurate, since such accuracy often requires more complex computations. Through a combination of new algorithms, new hardware, and your help, we’ve increased our capabilities by a million fold from when we first started. Between 2000 and 2010, the simulation timescales have increased by 1000x every five years, and the length of the proteins we’ve been able to study have doubled every five years. Just breaking past a microsecond was a big deal, and we’ve already performed several simulations out to several milliseconds – the timescale for most proteins. We’re really excited about where this appears to be leading, allowing us to tackle really challenging and important problems.
In order to study many of the problems of interest (especially related to protein misfolding and aggregation, such as in Alzheimer’s disease), we need to not just have lots of computers participating, but we need results returned more quickly so that we can simulate trajectories of sufficient length. Before the PS3, GPU, and SMP clients, we achieved this by running simulations for many months or even years (indeed, our first Alzheimer’s Disease simulations ran for almost two years straight). These new clients gave us considerably longer trajectories in the same wall clock time, allowing us to turn what used to take years to simulate even on FAH to a few weeks to months. Our goal is to apply our simulations to further our knowledge of protein folding, misfolding, and related diseases, including Alzheimer’s disease, Huntington’s disease, and certain forms of cancer. With these computational advances, coupled with new simulation methodologies to harness the new technologies, we will be able to address questions previously considered impossible to tackle computationally.
There are primarily two types of calculations we perform, which mainly differ by how we simulate water. The GPU client greatly speed “implicit solvation” simulations, in which water is handled mathematically in a continuum fashion. GPU technology continues to rapidly grow, and GPUs in particular have been a very powerful platform for our implicit solvation simulations. Our SMP client, (discussed in the SMP and High Performance FAQs) uses multiple CPU cores for a significant speed increase for “explicit solvation” simulations, where water atoms are handled atom by atom. Each platform has its limits, but together they work to give FAH considerably more computational power than ever before.
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Last Updated on December 24, 2012, at 05:11 PM