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.
Since 2006 we have been looking at methods to produce major advances in our capabilities. One of the technologies we pursued was multi-core CPUs in modern computers. SMP means “Symmetric Multi-Processing” and it is a term that generally refers to the situation where a computer has more than one processor core. It’s now very common for computers to contain multiple CPU cores, which allow the computer to process multiple sets of information in parallel. Most computers contain dual- or quad-core CPUs, and the higher-end machines can contain eight, sixteen, or more. Working together, SMP gives 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. For example, a quad-core CPU can complete Work Units nearly four times faster than a single-core CPU. Initially, it was a challenge to scale the GROMACS core — the highly optimized software that performs the actual protein folding simulations behind the scenes — to fully utilize multiple CPU cores, but our methods are now quite efficient at using them.
This has allowed us to address questions previously considered impossible to tackle computationally, and make even greater impacts on our knowledge of folding and folding related diseases. 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. By joining together hundreds of thousands of PCs throughout the world, calculations which were previously considered impossible have now become routine. Thanks to your help and these new technologies, Folding@home has remained one of the world’s most powerful computing systems. FAH has targeted the study of protein folding and protein folding disease, and numerous scientific advances have come from the project. We’re very excited about what the multi-core processors has been able to do so far. One of our papers (#53 in our Papers page) would have been impossible without the multi-core processors and represents a landmark calculation in the simulation of protein folding. We’re looking forward to more exciting results like that in the years to come!