We've teamed up with Simbios (an NIH Roadmap center at Stanford) to start making key technologies in Folding@home available for others to use. Our protein folding work has been named one of Simbios' key Driving Biology Problems (DBPs) and there's now a protein folding DBP page. Our hope is that our developments which have been so useful in Folding@home can help accelerate the work of others as well. Right now, there are two key areas we've been working to distribute, with more coming along the way.
First, we're working to make our GPU code available to others. This code will be distributed in a couple of forms. First, we'll give out a GPU-enabled version of Gromacs (basically, a standalone version of the GPU2 core), which will enable others to get major speed increases from GPUs. Next, we are working to release a GPU-enabled library (OpenMM), which will allow others to integrate GPU code into their programs. OpenMM is special in that it is a place for integrating both application developers as well as GPU vendors; much like OpenGL, our hope is that hardware acceleration vendors will now have a single API to accelerate and people who want to write applications will have a single, hardware accelerated API to use that would work on a variety of platforms.
Second, we're also starting to make large data sets from Folding@home available to others. You can see some of the first data sets on this project page, and we expect to put more data up as time goes on. Folding@home donors have generated wonderful data sets that aren't possible to generate by other means, and our hope is to publish them so that other scientists can data mine them for other purposes as well.
Finally, we expect to release more technologies from FAH. In particular, we will be releasing some of the key server-side algorithms which allow FAH to use lots of processors to tackle single complex problems. This will allow people to run massively parallel code on large clusters to do tasks similar to FAH (albeit on a smaller scale).