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Folding@home

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Folding@Home
Developer(s)Stanford University / Pande Group
Operating systemCross-platform
TypeDistributed computing
LicenseProprietary [1]
Websitefolding.stanford.edu

Folding@home (also known as FAH or F@H) is a distributed computing project designed to perform computationally intensive simulations of protein folding and other molecular dynamics simulations. It was launched on October 1, 2000, and is currently managed by the Pande Group, within Stanford University's Chemistry department, under the supervision of Professor Vijay S. Pande. F@H is one of the largest distributed computing projects.[1] The goal of the project is "to understand protein folding, misfolding, and related diseases."[2]

Accurate simulations of protein folding and misfolding enable the scientific community to better understand the development of many diseases, including Alzheimer's disease, BSE (mad cow disease), Cancer, Huntington's Disease, Cystic Fibrosis and other aggregation related diseases. [2] More fundamentally, understanding the process of protein folding -- how biological molecules assemble themselves into a functional state -- is one of the outstanding problems of molecular biology. So far, the F@H project has successfully simulated folding in the 5-10 microsecond range—a time scale thousands of times longer than was previously thought possible.[3]

As of November 9, 2006 45 scientific research papers have been published using the project's work.[4] A University of Illinois at Urbana-Champaign report dated October 22, 2002 states that F@H distributed simulations of protein folding are demonstrably accurate.[5]

How it works

Folding@home does not rely on powerful supercomputers for its data processing; instead, the primary contributors to the F@H project are many hundreds of thousands of personal computer users who have installed a small client program. The client will, at the user's choice, run in the background utilizing otherwise unused CPU power, or run as a screensaver only while the user is away. In most modern personal computers, the CPU is rarely used to its full capacity at all times; the F@H client takes advantage of this unused processing power.

The F@H client periodically connects to a server to retrieve "work units," which are packets of data upon which to perform calculations. Each completed work unit is then sent back to the server. As data integrity is a major concern for all distributed computing projects, all work units are validated through the use of a 2048 bit digital signature.

The F@H client utilizes modified versions of four molecular simulation programs for calculation: TINKER, GROMACS, AMBER, and CPMD.[6]

Contributors to F@H may have user names used to keep track of their contributions. Each user may be running the client on one or more CPUs; for example, a user with two computers could run the client on both of them. Users may also contribute under one or more team names; many different users may join together to form a team. Contributors are assigned a score indicating the number and difficulty of completed work units. Rankings and other statistics are posted to the F@H website.

Participation

Folding@home FLOPS by platform as of 10 January 2007. Note that at this date, the GPU client is in beta stage and is only available for the Windows XP operating system

As of January 2007, more than 183,000 CPUs were actively participating in Folding@home (active CPUs are defined as those returning work units within the last 50 days), jointly capable of a sustained computational level of around 220 teraFLOPS, and with over 1,800,000 CPUs registered.[1] This level of participation makes the F@H distributed supercomputer one of the most powerful supercomputers in the world; if viewed as a single supercomputer, the F@H project would rank second in the TOP500 list.

Shortly after breaking the 200,000 active CPU count on September 20, 2005, the F@H project celebrated its fifth anniversary on October 1, 2005.

Folding@home average megaFLOP per individual client type. One single GPU accounts for as much processing power as about 60 CPUs under Windows. Note that this is not a measure of raw processing power, but depends on a variety of other factors such as uptime and CPU load and speed. This is important because older processors are still running the CPU clients while only more modern GPUs are capable of running the GPU client accounting for the large difference in flops per client

Future changes to the weight of Windows users in F@H are uncertain. On the one hand, the beta clients for 64-bit SMP computers might increase the shares for OS X/Intel and Linux (at least until a port is made available for Windows), and the upcoming Cure@PS3 client for PlayStation 3 is expected to make a hefty contribution. On the other hand, should the GPU beta client be made available for graphics cards other than ATI, it should boost Windows TeraFLOPS even more (See the Platforms section below.)

Google & Folding@home

There used to be cooperation between Folding@home and Google Labs. This came in the form of Google Compute. Google Compute supported F@H when it was at an early stage -- when F@H had ~10,000 active CPUs. At that time, a boost of 20,000 machines was very significant. Now, the F@H client is considerably more mature than it was 5 years ago, and the project has a large number of active CPUs. The number of new clients joining Google Compute was very low (most people opted for the F@H client instead) and so it didn't make sense to continue it. Also, the Google Compute clients had certain limits: they could only run the TINKER core, limited naming, and team options. F@H is no longer supported on Google Toolbar, and even the old Google Toolbar client will not work.[7]

High performance platforms

Graphical processing units

Current research is aimed at accelerating computational power by utilizing a computer's graphics processing unit (GPU) in addition to the Central processing unit (CPU). News about the progress of porting Folding@home onto GPUs can be found in the "High performance client FAQ" section of the F@H FAQ pages.[8] Recent test data indicate performance gains of up to 40x that of an Intel Pentium 4 CPU are possible. (Note: this performance varies with different GPUs). Stanford has recently cited further advances with the high performance client and released a public, beta trial at the end of September 2006. However, this trial is specific to ATI Technologies' GPUs due to the performance characteristics of the processors for this application.[9]

As of October 2, 2006, the F@H GPU client has been released into a public beta test. After 9 days of processing from the Beta client the F@H project had received 31 teraFLOPS of computational performance from just 450 X1900 GPUs, averaging at over 70x the performance of current CPU submissions.[1]

PlayStation 3

Stanford announced in August 2006 that a folding client will be available to run on the PlayStation 3 called Cure@PS3.[10] The intent is that gamers be able to contribute to the project by merely "contributing electricity," leaving their PlayStation 3 consoles running the client while not playing games. It is estimated that with the addition of approximately 10,000 machines the Folding@home project would be able to reach petaflop scale.

Multi core processing client

As more modern CPUs are being released the migration to multiple cores is becoming more adopted by the public, the Pande Group is finally adding the Symmetric multiprocessing (SMP) support to the Folding@home client as well in hopes to capture the additional processing power. On November 13, 2006, the beta SMP Folding@home clients for x86-64 Linux and x86-64 Mac OS X have been released.[11]

Folding@home teams

A typical Folding@home user, running the client on a single PC, will likely not be ranked high on the list of contributors. However, if the user were to join a team, they would add the points they receive to a larger collective. Teams work by using the combined score of all their members. Thus, teams are ranked much higher than individual submitters. Rivalries between teams create friendly competition that benefits the folding community. Many teams publish their own stats, so members can have inter-team competitions for top spots. Teams offer no real benefits other than ones of self-gratification.[12]

The future of Folding@home

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With a published history of 40 peer-reviewed papers (June 2006) from the Pandegroup at Stanford, and 30 more prior to that, it seems scientific worth is not a problem.

The science behind folding is continually evolving, and more people want to do research in this field. The PI list for the folding project changes from year to year as students complete their study and new recruits take over.

The folding project is always looking to push the boundaries of technology too, provided that resulting increase in computing power is worth the initial investment.

  • Clients/cores designed to take advantage of the processing power of GPUs are already being worked upon, as is integrating SMP into the Gromacs core.
  • The existing processing cores are updated on a regular basis in order to incorporate the latest features of their respective simulation methods.

Vijay himself has already said that the research he and his group are doing is still limited by the raw CPU power available to them:

We have specific plans ready to put into action once we reach certain CPU levels, up to 1M[illion] CPUs. Once we start to get close to 1M active CPUs, we will sit down and make plans for going beyond that. Our research is very computer limited.

See also

Notes and references

  1. ^ a b c "Client Statistics by OS". Folding@home distributed computing. Stanford University. 2006-11-12 (updated automatically). Retrieved 2006-11-12. {{cite web}}: Check date values in: |date= (help)
  2. ^ Vijay Pande (2006). "Folding@home distributed computing home page". Stanford University. Retrieved 2006-11-12.
  3. ^ "Validity of Folding@home" (Blog). Folding@home support forum. Stanford University. Retrieved 2006-11-12.
  4. ^ Vijay Pande (2006). "Recent Pande Group research papers". Folding@home distributed computing. Stanford University. Retrieved 2006-11-12.
  5. ^ C. Snow, H. Nguyen, V. S. Pande, and M. Gruebele. (2002). "Absolute comparison of simulated and experimental protein-folding dynamics". Nature. 420 (6911): 102–106. PMID 12422224.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  6. ^ Vijay Pande (2005-10-16). "Folding@Home with QMD core FAQ" (FAQ). Stanford University. Retrieved 2006-12-03. The site indicates that Folding@home uses a modification of CPMD allowing it to run on the supercluster environment.
  7. ^ "What is the state of Google Compute client?" (Blog). Folding@home support forum. Stanford University. Retrieved 2006-11-12.
  8. ^ "Folding@home high performance client FAQ". FAQs on new hardware, Folding@home. Vijay Pande and Stanford University. Retrieved 2006-12-01.
  9. ^ Vijay Pande (2006-11-06). "Folding@home on ATI GPU FAQ". Stanford University. Retrieved 2006-11-13.
  10. ^ Vijay Pande (2006-10-22). "PS3 FAQ". Stanford University. Retrieved 2006-11-13.
  11. ^ Vijay Pande (2006-11-13). "Folding@home SMP Client FAQ". Stanford University. Retrieved 2006-11-13.
  12. ^ Folding-community: why have teams?
  • M. R. Shirts and V. S. Pande. (2000). "Screen Savers of the World, Unite!". Science. 290: 1903–1904.
  • C. Snow, H. Nguyen, V. S. Pande, and M. Gruebele. (2002). "Folding of a bba protein: simulation and theory". Nature. 420: 102–106.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  • C. D. Snow, E. J. Sorin, Y. M. Rhee, and V. S. Pande. (2005). "How well can simulation predict protein folding kinetics and thermodynamics?". Annual Reviews of Biophysics. 34: 43–69.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  • L. T. Chong, C. D. Snow, Y. M. Rhee, and V. S. Pande. (2004). "Dimerization of the p53 oligomerization domain: Identification of a folding nucleus by molecular dynamics simulations". Journal of Molecular Biology. 345: 869–78.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  • I. Suydam, C. D. Snow, V. S. Pande and S. G. Boxer. (2006). "Electric Fields at the Active Site of an Enzyme: Direct Comparison of Experiment with Theory". Science. in press.{{cite journal}}: CS1 maint: multiple names: authors list (link)
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