The performance of the Folding@home (FAH) software is critical to the success of the Folding@home project. 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. When FAH first started, 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). However, we want to tackle problems that could take even longer, but those projects wouldn’t be practical if we had to wait many years for all the results to come back. This suggests the need to find methods which can perform the simulations even faster.
Multi-core CPUs and powerful graphics cards are now common, and we’ve seen significant gains in our capabilities by utilizing these technologies in our latest software, the V7 client. In the past, they were each supported by separate software programs. Now, if your computer supports these technologies, the V7 client can use them by default.
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. These new technologies give 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. 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 breakthroughs in 2007, it has remained one of the world’s most powerful computing systems.
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. Every five years the length of the proteins we’ve been able to study have doubled, while the simulation timescales increase by 1000x. 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.