Folding@home

Planned server room maintenance: Saturday, November 10th, from 5AM to 8AM

One of our key server rooms will undergo network maintenance on Saturday, November 10th, from 5AM to 8AM pacific time.  During the maintenance window, we expect that the servers in that room will be unreachable, hopefully for only 30 minutes each, but potentially for the full time range.  We note that this is only one of our server rooms, so the FAH backend should still be primarily operational, but some donors will see some issues with returning work during this time.  We also stress that the FAH server backend is architected such that even when servers are down, the points for donors will not be lost, and just the accounting for those points will be delayed until the servers are back up. 

Update on on-going software development in FAH

We have several on-going software development efforts and I'd like to give donors an update.

v7 client. Joe Coffland and his team have been working hard on new client releases. 7.2.9 has just been released and a new version will be undergoing beta testing soon. Moreover, we are continuing work on improving the v7 client for windows and squashing the remaining bugs. Moreover, there's additional effort in OSX due to the hiring of a programmer (Kevin Bernhagen) just for the OSX client, as well as additional work for smoother OSX and linux installs.

Gromacs core. The Gromacs core team (Prof. Michael Shirts and Prof. Peter Kasson and their labs, at the University of Virginia) are working on the new cores based on the new version of gromacs (4.6).

New OpenMM core. The OpenMM team at Stanford (Dr. Peter Eastman and Yutong Zhao) are working on speed improvements for OpenMM (the basis of the FAH GPU core) in general, but in particular optimizations for Kepler and AMD (in coordination with engineers at NVIDIA and AMD, respectively). Yutong has a new FAH GPU core working in the lab and we are doing internal testing on it.  Since openMM is full open source, you can see more details, including a commit and change log, at the openMM web site (https://simtk.org/home/openmm).

New FAH viral ad campaign.  We're also working on a new landing page for FAH and a new video to advertise FAH.  This new web/video campaign is coordinated with new client/installer changes to make FAH easier to install and run, especially for those new to Folding@home.

We understand that donors don't get to see all of what's going behind the scenes, so we'll try to post these sorts of updates more frequently.

Today, FAH is the featured article on Wikipedia

Today, FAH is the featured article on Wikipedia.  We mentioned this earlier, but here's the snapshot.  Thanks to everyone who've contributed to making this possible!

Version 7.2.9 of the Folding@home software is now available

We are happy to announce that version 7.2.9 of the Folding@home software is now available at folding.stanford.edu. The ultimate goal of Folding@home has always been to support disease research but we've also understood that one of the best ways to do this is to make folding fun and easy. This is why we continual push to make the Folding@home software easier to install and use while still adding advanced features for power users optimizing their machines for maximum points. This software release makes strides forward on all fronts.

One of the more visually interesting and fun features is our much improved 3D viewer. It is now able to display proteins from many more folding projects, works on more graphics cards and uses less CPU time. This code is also being used as the basis for the upcoming screensaver which is currently in testing.

Those with the latest and greatest hardware will be glad to know that this version supports automatic updating of the GPU white-list. In practical terms, this means Folding@home will be able to support new graphics hardware more quickly, as it becomes available and with out requiring new software. We will publish a new GPU white-list periodically and the client will automatically update its list of supported GPUs on startup.

We are also proud to announce that we recently hired OSX guru Kevin Bernhagen. He has been working hard to bring the OSX release up to our standards. With the v7.2.9 release we are taking the v7 software for OSX out of beta for the first time. There is still much work to do but this release should offer many improvements over the v6 beta that was previously the recommend release for OSX and should integrate better with the OSX environment better than the previous beta software.

Many other improvements have been made. The folding forum thread titled FAHClient V7.2.9 (8th Open-Beta) lists the changes in more detail. While you're at it, join in the forum discussions and let us know if you run into problems, have a great idea or just what to tell us about the awesome new folding rig you've setup.

We are far from done improving Folding@home. Many new features are in the works including simpler installers, even better OSX support and easier more intuitive user interfaces. There are also some surprises on the drawing board which we are very excited about but not yet ready to announce. So stay tuned and thank you for continuing to help support disease research at Stanford and universities all over the world.

 

The Folding@home article on Wikipedia is now a Featured Article

Here's a guest post from Jesse Victors, one of the volunteers helping with Folding@home documentation.

I am pleased to announce that the Folding@home article on Wikipedia is now a Featured Article. Following a peer review and a thorough month-long discussion, other editors agreed that it met the Featured Article criteria. This means that it's encyclopedic, well written, comprehensive, well researched, neutral, stable, and uses summary style. If you are new to the Folding@home project, or would like to learn more, I would highly recommend that you read this article. There are plenty of details on all aspects of Folding@home, but I tried to avoid technical language so it should be understandable by almost anyone. I've also summarized many of publications from FAH (see the Papers page) as well as other scientific literature, which can be helpful if you're curious about how this project works or what it has accomplished so far. The article is currently scheduled to appear on Wikipedia's Main Page as a Featured Article of the Day on November 1st, so keep an eye out for that!

Life with Playstation ending, FAH team continuing to look to push the envelope

For several years, we have worked closely with Sony to bring Folding@home to the PS3.  We're excited about what we've been able to do.  Since the PS3 started folding in 2007, we've done some really amazing things, with several announcements this year acknowledging advancements in Alzheimer's Disease, Cancer (and this link), Influenza, Type II Diabetes, and other new drug targets. We've come a long way in the last 5 years and we have a lot going on to continue our tradition of pushing the envelope into new technologies.

Unified GPU/SMP benchmarking scheme: equal points for equal work

The current benchmarking calculations for SMP and GPU projects are performed on different machines since originally the SMP cores could not perform the calculations that the GPUs cores could and vice versa (GPUs were only for implicit solvent calculations and SMP only for explicit solvent calculations). With recent advances in both cores and completion of our testing of these capabilities to ensure agreement, we are now confident we can do the same work on both cores. Thus, we feel that it is time to unify GPU and SMP benchmarking, both for simplicity and fairness.

In order to complete the move towards this plan of "equal points for equal work," new GPU projects will be benchmarked using the existing SMP benchmarking scheme. Based on our internal tests, the end effect of this new, unified benchmarking scheme would boost the points for the GPU projects, both in terms of base points but also by bringing Quick Return Bonuses to GPU clients. In order to test the new scheme, we have started a GPU3 project (Project ID: 8057) and released it for beta testing. Once the benchmarking scheme has been tested, all the current GPU projects will be re-benchmarked to reflect the changes in the benchmarking scheme. 

New Gromacs, new you

Guest post from Profs Kasson and Shirts, UVA and Mr. Coffland

A new version of Gromacs (4.6) is coming, and we’re working to bring it to Folding@home.  The new code contains a number of improvements (more than you’d expect for a minor version number!), and we’ll post about some of the individual features as we go.  Not all of them will be available on F@h immediately, as some will require substantial development work over the next few months.  But some of the basics are new free energy methods from our very own Prof. Michael Shirts, new and slightly faster inner-loop code, and some important tweaks to parallelization.  Free energy calculations allow us to calculate things like how tightly drugs bind to proteins and the strength of attraction between protein components when pulled apart.  And you, of course, know what faster inner-loop code and better parallelization mean!

Gromacs is an interesting piece of simulation software in that it’s heavily optimized both for single-computer performance (part of why we chose it for F@h in the first place) and for parallel scaling.  A lot of codes choose to emphasize one or the other.  But Gromacs tries to do both.  That will have some interesting and useful implications for F@h particularly as we look at more and more cores on donor CPU’s (and things like GPU integration).  That’s all for now; we’ll keep you posted on progress.

Thanks!
The 4.6 Core Team (Profs. Kasson, Shirts, and the indefatigable Mr. Coffland)

Futures in Biotech video interview about FAH

This video is a year old (and we've previously posted the audio), but looking at this again, a lot is still relevant (and we didn't post the video url), so here it is in case people are curious:

http://www.youtube.com/watch?v=mhw95cBzk6U

Host: Marc Pelletier
Dr. Vijay Pande, Stanford's Director of Folding@home, details how the World's most powerful system models Alzheimer's and other human diseases.
Guest: Dr. Vijay Pande
We invite you to read, add to, and amend our show notes.
Comments and suggestions on Futures in Biotech.
Also thanks to Phil Pelletier and Will Hall for the great themes.
Thanks to Cachefly for providing the bandwidth for this netcast.
Running time: 1:17:43

New methods for analyzing FAH data

Guest post from Dr. Gregory Bowman, UC Berkeley

Two general objectives of the Folding@home project are (1) to explain the molecular origins of existing experimental data and (2) to provide new insights that will inspire the next generation of cutting edge experiments.  We have made tremendous progress in both areas, but particularly in the first area.  Obtaining new insight is even more of an art and, therefore, less automatable. 

To help facilitate new insights, I recently developed a Bayesian algorithm for coarse-graining our models.  To explain, when we are studying some process—like the folding of a particular protein—we typically start by drawing on the computing resources you share with us to run extensive simulations of the process.  Next, we build a Markov model from this data.  As I’ve explained previously, these models are something like maps of the conformational space a protein explores.  Specifically, they enumerate conformations the protein can adopt, how likely the protein is to form each of these structures, and how long it takes to morph from one structure to another.  Typically, our initial models have tens of thousands of parameters and are capable of capturing fine details of the process at hand.  Such models are superb for making a connection with experiments because we can capture all the little details that contribute to particular experimental observations.  However, they are extremely hard to understand.  Therefore, it is to our advantage to coarse-grain them.  That is, we attempt to build a model with very few parameters that is as close as possible to the original, complicated model.  If done properly, the new model can capture the essence of the phenomenon in a way that is easier for us to wrap our minds around.  Based on the understanding this new model provides, we can start to generate new hypotheses and then test them with our more complicated models and, ultimately, via experiment.

Statistical uncertainty is a major hurdle in performing this sort of coarse-graining.  For example, if we observe 100 transitions between a pair of conformations and each of these transitions is slow, then we can be pretty sure this is really a slow transition.  However, if we only observe another transition once and it happens to occur slowly, who knows?  It could be that it is really a slow transition.  On the other hand, it could be we just got unlucky. 

Existing methods for coarse-graining our Markov models assume we have enough data to accurately describe each transition.  Therefore, they often pick up these poorly characterized transitions as being important (for protein folding, we typically care most about the slow steps, so slow and important are synonymous).  The new method I’ve developed (described here) explicitly takes into account how many times a transition was observed.  Therefore, it can appropriately place emphasis on the transitions we observed enough times to trust while disregarding the transitions we don’t trust.  To accomplish this, I draw on Bayesian statistics.  I can’t do this subject justice here, but if you’re ever trying to make sense of data that you have varying degrees of faith in, I highly recommend you look into Bayesian statistics.