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Folding@home

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Folding@Home
Original author(s)Vijay Pande
Developer(s)Stanford University / Pande Group
Stable release
5.03 (Windows),
5.02 (Linux),
5.02 (Mac OS X),
1.2 (PlayStation 3)
Preview release
5.04 (Windows),
5.04 (Linux),
5.91beta4 (GPU),
5.91 (Windows-SMP),
5.91 (Mac OS X-SMP),
5.91 (Linux-SMP) / 2006-12-01
PlatformCross-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. Folding@home 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 Folding@home project has successfully simulated folding in the 5-10 microsecond range — a time scale thousands of times longer than it was previously thought possible to model.[3]

As of June 25, 2007, fifty 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 Folding@home distributed simulations of protein folding are demonstrably accurate.[5]

How it works

Folding@Home when running takes advantage of unused CPU cycles on a computer system as shown by this computer's 100% CPU usage.

Folding@home does not rely on powerful supercomputers for its data processing; instead, the primary contributors to the Folding@home 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 Folding@home client takes advantage of this unused processing power.

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

Contributors to Folding@home 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 Folding@home website.

Participation

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

As of May 31, 2007 the peak speed of the project overall has reached over 1 PFLOPS.[7]

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 Folding@home during its early stage — when Folding@home had ~10,000 active CPUs. At that time, a boost of 20,000 machines was very significant. Now, the Folding@home 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 Folding@home 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. Folding@home is no longer supported on Google Toolbar, and even the old Google Toolbar client will not work.[8]

High performance platforms

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.

Graphical processing units

As of October 2, 2006, the Folding@home GPU client was been released into a public beta test. After 9 days of processing from the Beta client the Folding@home 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

File:PS3-FoldingatHome.jpg
The PlayStation 3's Folding@Home client was made available on March 22, 2007.

Stanford announced in August 2006 that a folding client was available to run on the Sony PlayStation 3.[9] The intent was that gamers would be able to contribute to the project by merely "contributing electricity," leaving their PlayStation 3 consoles running the client while not playing games. PS3 firmware version 1.60 (released on Thursday, March 22, 2007) allows for Folding@home software, a 50 MB download, to be used on the PS3.[10] A peak output of the project at 990 teraFLOPS was achieved on 25 March, 2007, at which time the number of FLOPS from each PS3 as reported by Stanford fell, reducing the overall speed rating of those machines by 50%. This had the effect of bumping down the overall project speed to the mid 700 range and increasing the number of active PS3's required to achieve a petaFLOPS level to around 60,000. Lately, the console accounts for about 60% of all teraFLOPS. On April 25 2007, Sony announced that a new version of Folding@home would be released the next day. The new version would improve folding performance beyond the current capacity, far beyond even the 400 teraFLOPS previously reached by PS3 users.[11]

Multi-core processing client

File:FAH-SMP.jpg
Folding@Home SMP Client set to use 95% an Intel Q6600 Quad Core 2 Prcessor (4x 2.4Ghz).

As more modern CPUs are being released the migration to multiple cores is becoming more adopted by the public, the Pande Group is 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 Mac OS X have been released. The beta win32 SMP Folding@home client is out as well, and a 32-bit Linux client is currently in development.[12]

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 intra-team competitions for top spots. Teams offer no real benefits other than ones of self-gratification, and possibly extra contributions (to add to the teams rank).[13]

Source code

The Folding@home project does not make the project source code available to the public, citing security and integrity concerns. At the same time, the majority of the scientific codes used by the FAH (ex. Cosm, Gromacs, TINKER, AMBER, CPMD, BrookGPU) are largely OSS or under similar licenses.

See also

Notes and references

  1. ^ a b "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 (2007). "Recent Pande Group research papers". Folding@home distributed computing. Stanford University. Retrieved 2007-03-30.
  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. ^ http://fah-web.stanford.edu/cgi-bin/main.py?qtype=osstats
  8. ^ "What is the state of Google Compute client?" (Blog). Folding@home support forum. Stanford University. Retrieved 2006-11-12.
  9. ^ Vijay Pande (2006-10-22). "PS3 FAQ". Stanford University. Retrieved 2006-11-13. {{cite web}}: Check date values in: |date= (help)
  10. ^ http://fah-web.stanford.edu/cgi-bin/main.py?qtype=osstats
  11. ^ http://kotaku.com/gaming/folding%40home/ps3-folding-kicking-ass-getting-update-255086.php
  12. ^ Vijay Pande (2006-11-13). "Folding@home SMP Client FAQ". Stanford University. Retrieved 2006-11-13.
  13. ^ 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)
  • Folding-community: How can you tell the true nature of a Work Unit
  • Folding-community: Vijay - No need to report EUEs

External links

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