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drepanocytosis

Folding@home

Folding@home (sometimes abbreviated as FAH or F@h) is a distributed computing (DC) project designed to perform computationally intensive simulations of protein folding and other molecular dynamics (MD). 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 Pande. Folding@home is the most powerful distributed computing cluster in the world, according to Guinness, and one of the world's largest distributed computing projects. The goal of the project is "to understand protein folding, misfolding, and related diseases.

Purpose

Accurate simulations of protein folding and misfolding enable the scientific community to better understand the development of many diseases, including sickle-cell disease (drepanocytosis), Alzheimer's disease, Parkinson's disease, mad cow disease, cancer, Huntington's disease, cystic fibrosis, osteogenesis imperfecta, alpha 1-antitrypsin deficiency, and other aggregation-related diseases. 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. The Pande Group goal is to refine and improve the MD and Folding@home DC methods to the level where it will become an essential tool for the MD research. For that goal they collaborate with various scientific institutions. As of August 10, 2008, fifty-seven scientific research papers have been published using the project's work. A University of Illinois at Urbana-Champaign report dated October 22, 2002 states that Folding@home distributed simulations of protein folding are demonstrably accurate.

Function

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.

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.

Analysis Software

The Folding@home client utilizes modified versions of four molecular simulation programs for calculation: TINKER, GROMACS, AMBER, and CPMD. Where possible, optimizations are used to speed the process of calculation. There are many variations on these base simulation programs, each of which is given an arbitrary identifier (Core xx):

Active Cores

  • GROMACS (all variants of this core use SIMD optimizations including SSE, 3DNow+ or AltiVec, where available, unless otherwise specified)
    • Gromacs (Core 78)
      • Available for all Uniprocessor clients only.
    • DGromacs (Core 79)
      • Double Precision Gromacs, uses SSE2 only.
      • Available for all Uniprocessor clients only.
    • DGromacsB (Core 7b)
      • Nominally an update of DGromacs, but is actually based on the SMP/GPU codebases (and is therefore a completely new core). As a result, both are still in use.
      • Double Precision Gromacs, uses SSE2 only.
      • Available for all Uniprocessor clients only.
    • DGromacsC (Core 7c)
      • Double Precision Gromacs, uses SSE2 only.
      • Available on Windows and Linux Uniprocessor clients only.
    • GBGromacs (Core 7a)
      • Gromacs with the Generalized Born implicit solvent model.
      • Available for all Uniprocessor clients only.
    • Gromacs SREM (Core 80)
      • Gromacs Serial Replica Exchange Method.
      • The Gromacs Serial Replica Exchange Method core, also known as GroST (Gromacs Serial replica exchange with Temperatures), uses Replica Exchange Method (also known as REMD or Replica Exchange Molecular Dynamics) in its simulations.
      • Available for Windows and Linux Uniprocessor clients only.
    • GroSimT (Core 81)
      • Gromacs with Simulated Tempering.
      • Available for Windows and Linux Uniprocessor clients only.
    • Gromacs 33 (Core a0)
      • Uses the newer Gromacs 3.3 codebase.
      • Available for all Uniprocessor clients only.
    • Gro-SMP (Core a1)
      • Symmetric MultiProcessing variant, locked to four threads (but can be run on dual core processors).
      • Runs only on multi-core x86 or x64 hardware, uses SSE only.
      • Available for all SMP clients only.
    • GroCVS (Core a2)
      • Symmetric MultiProcessing variant with scalable numbers of threads.
      • Runs only on multi-core x86 or x64 hardware, with four or more cores, uses SSE only.
      • Uses the Gromacs 3.3.99 codebase (the latest stable release at the time of coding).
      • Available for all SMP clients only.
    • GroGPU2 (Core 11)
      • Graphics Processing Unit variant for ATI CAL-enabled and nVidia CUDA-enabled GPUs.
      • Comes in two separate versions, one each for ATI and nVidia, but both have the same Core ID.
      • GPUs do not have optimizations.
      • Available for GPU2 client only.
    • Gro-PS3 (Does not have a known ID number, but also called SCEARD core)
      • PlayStation 3 variant.
      • No SIMD optimizations, uses SPE cores for optimization.
      • Available for PS3 client only.
  • AMBER
    • PMD (Core 82)
      • No optimizations.
      • Available for Windows and Linux Uniprocessor clients only.

Inactive Cores

  • TINKER
    • Tinker core (Core 65)
      • Currently inactive, as the GBGromacs core (Core 7a) performs the same tasks much faster.
      • No optimizations.
      • Available for all Uniprocessor clients only.
  • GROMACS
    • GroGPU (Core 10)
      • Graphics Processing Unit variant for ATI series 1xxx GPUs.
      • GPUs do not have optimizations; no SIMD optimizations needed since GPU cores are explicitly designed for SIMD.
      • Inactive as of June 6, 2008 due to end of distribution of GPU1 client units.
      • Available for GPU1 client only.
  • CPMD
    • QMD (Core 96)
      • Currently inactive, due to QMD developer graduating from Stanford University and due to current research shifting away from Quantum MD.
      • Caused controversy due to SSE2 issues involving Intel libraries and AMD processors.
      • Uses SSE2 (currently only on Intel CPUs, see above).
      • Available for Windows and Linux Uniprocessor clients only.

Possible future additions

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.

Interest and participation in the project has grown steadily since its launch. The number of active devices participating in the project increased substantially after receiving much publicity during the launch of their high performance clients for both ATi graphics cards and the PlayStation 3, and again following the launch of the high performance client for nVidia graphics cards.

As of October 3, 2008 the peak speed of the project overall has reached over 4.1 PFLOPS from over 340,000 active machines, and the project has received computational results from over 3.3 million devices since it first started.

PetaFLOPS Milestones

On September 16, 2007, the Folding@home project officially attained a performance level higher than one petaFLOPS, becoming the first computing system of any kind to do so, although it had briefly peaked above one petaFLOPS in March 2007.. In comparison, the fastest supercomputer in the world (as of June 2008, IBM's Roadrunner) peaks at 1.026 petaFLOPS. In early May 2008 the project attained a sustained performance level higher than two petaFLOPS, followed by the three and four petaFLOPS milestones on August 20 and September 28, 2008 respectively. For all four milestones Folding@home was the first computing system of any kind to do so. The Folding@home computing cluster operates at above 4 petaFLOPS at all times, with a large majority of the performance coming from PlayStation 3 and GPU clients.

Google & Folding@home

There used to be cooperation between Folding@home and Google Labs in the form of Google Toolbar. 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. Today the project has a large number of active CPUs and the number of new clients joining Google Compute was very low (most people opted for the Folding@home client instead), so it was discontinued. The Google Compute clients also had certain limits: they could only run the TINKER core and had limited naming and team options. Folding@home is no longer supported on Google Toolbar, and even the old Google Toolbar client will not work.

Genome@home

Folding@home absorbed the Genome@home project on March 8, 2004. The work which was started by the Genome@home project has since been completed using the Folding@home network (the work units without deadlines), and no new work is being distributed by this project. All donators were encouraged to download the Folding@home client (the F@h 4.xx client had a Genome@home option), and once the Genome@home work was complete these clients were asked to donate their processing power to the Folding@home project instead.

Results

These peer-reviewed papers (in chronological order) all use research from the Folding@home project.

2000-2001

  • "Screen savers of the world, Unite!", Michael R. Shirts and Vijay Pande, Science (2000)
  • "Mathematical Foundations of ensemble dynamics", Michael R. Shirts and Vijay Pande, Physical Review Letters (2001)
  • "b-Hairpin Folding Simulations in Atomistic Detail Using an Implicit Solvent Model", Bojan Zagrovic, Eric J. Sorin, and Vijay Pande, Journal of Molecular Biology (2001)

2002

  • "Atomistic protein folding simulations on the submillisecond timescale using worldwide distributed computing", Vijay Pande, et al. Peter Kollman Memorial Issue, Biopolymers (2002)
  • "Folding@home and Genome@home: Using distributed computing to tackle previously intractable problems in computational biology", Stefan M. Larson, Christopher D. Snow, Michael R. Shirts, and Vijay S. Pande. To appear in Computational Genomics, Richard Grant, editor, Horizon Press (2002)
  • "Simulation of Folding of a Small Alpha-helical Protein in Atomistic Detail using Worldwidedistributed Computing", Bojan Zagrovic, Christopher D. Snow, Michael R. Shirts, and Vijay S. Pande. Journal of Molecular Biology (2002)
  • "Native-like Mean Structure in the Unfolded Ensemble of Small Proteins", Bojan Zagrovic, Christopher D. Snow, Siraj Khaliq, Michael R. Shirts, and Vijay S. Pande. Journal of Molecular Biology (2002)
  • "Absolute comparison of simulated and experimental protein-folding dynamics", Christopher D. Snow, Houbi Ngyen, Vijay S. Pande, and Martin Gruebele. Nature (2002)
  • "The Trp Cage: Folding Kinetics and Unfolded State Topology via Molecular Dynamics Simulations", Christopher D. Snow, Bojan Zagrovic, and Vijay S. Pande. Journal of the American Chemical Society (2002)

2003

  • "Multiplexed-Replica Exchange Molecular Dynamics Method for Protein Folding Simulation", Young Min Rhee & Vijay S. Pande. Biophysical Journal (2003)
  • "Insights Into Nucleic Acid Conformational Dynamics from Massively Parallel Stochastic Simulations", Eric J. Sorin, Young Min Rhee, Bradley J. Nakatani & Vijay S. Pande. Biophysical Journal (2003)
  • "Solvent Viscosity Dependence of the Folding Rate of a Small Protein: Distributed Computing Study", Bojan Zagrovic and Vijay S. Pande. Journal of Computational Chemistry (2003)
  • "Extremely precise free energy calculations of amino acid side chain analogs: Comparison of common molecular mechanics force fields for proteins", Michael R. Shirts, Jed W. Pitera, William C. Swope, and Vijay S. Pande. Journal of Chemical Physics (2003)
  • "Equilibrium Free Energies from Nonequilibrium Measurements Using Maximum-Likelihood Methods", Michael R. Shirts, Eric Bair, Giles Hooker, and Vijay S Pande. Physical Review Letters (2003)
  • "Structural correspondence between the alpha-helix and the random-flight chain resolves how unfolded proteins can have native-like properties", Bojan Zagrovic & Vijay S Pande. Nature Structural Biology (2003)

2004

  • "Does Native State Topology Determine the RNA Folding Mechanism?", Eric J. Sorin, Bradley J. Nakatani, Young Min Rhee, Guha Jayachandran, V Vishal, & Vijay S Pande. Journal of Molecular Biology (2004)
  • "Trp zipper folding kinetics by molecular dynamics and temperature-jump spectroscopy", Christopher D. Snow, Linlin Qiu, Deguo Du, Feng Gai, Stephen J. Hagen, & Vijay S Pande. Proceedings of the National Academy of Sciences, USA (2004)
  • "Simulations of the role of water in the protein-folding mechanism", Young Min Rhee, Eric J. Sorin, Guha Jayachandran, Erik Lindahl, & Vijay S Pande. Proceedings of the National Academy of Sciences, USA (2004)
  • "Using path sampling to build better Markovian state models: Predicting the folding rate and mechanism of a tryptophan zipper beta hairpin", Nina Singhal, Christopher D. Snow, and Vijay S. Pande. Journal of Chemical Physics (2004)

2005

  • "Dimerization of the p53 Oligomerization Domain: Identification of a Folding Nucleus by Molecular Dynamics Simulations", Lillian T. Chong, Christopher D. Snow, Young Min Rhee, and Vijay S. Pande. Journal of Molecular Biology (2005)
  • "Does Water Play a Structural Role in the Folding of Small Nucleic Acids?", Eric J. Sorin, Young Min Rhee, and Vijay S. Pande. Biophysical Journal (2005)
  • "Exploring the Helix-Coil Transition via All-atom Equilibrium Ensemble Simulations", Eric J. Sorin and Vijay S. Pande. Biophysical Journal (2005)
  • "Empirical Force-Field Assessment: The Interplay Between Backbone Torsions and Noncovalent Term Scaling", Eric J. Sorin and Vijay S. Pande. Journal of Computational Chemistry (2005)
  • "How well can simulation predict protein folding kinetics and thermodynamics?", Christopher D. Snow, Eric J. Sorin, Young Min Rhee, and Vijay S. Pande. Annual Review of Biophysics & Biomolecular Structure (2005)
  • "Unusual compactness of a polyproline type II structure", Bojan Zagrovic, Jan Lipfert, Eric J. Sorin, Ian S. Millett, Wilfred F. van Gunsteren, Sebastian Doniach & Vijay S. Pande. Proceedings of the National Academy of Sciences, USA (2005)
  • "Foldamer dynamics expressed via Markov state models. II. State space decomposition", Sidney Elmer, Sanghyun Park, & Vijay S. Pande. Journal of Chemical Physics (2005)
  • "Foldamer dynamics expressed via Markov state models. I. Explicit solvent molecular-dynamics simulations in acetonitrile, chloroform, methanol, and water", Sidney Elmer, Sanghyun Park, & Vijay S. Pande. Journal of Chemical Physics (2005)
  • "Solvation free energies of amino acid side chain analogs for common molecular mechanics water models", Michael R. Shirts & Vijay S. Pande. Journal of Chemical Physics (2005)
  • "Comparison of efficiency and bias of free energies computed by exponential averaging, the Bennett acceptance ratio, and thermodynamic integration", Michael R. Shirts & Vijay S. Pande. Journal of Chemical Physics (2005)
  • "A New Set of Molecular Mechanics Parameters for Hydroxyproline and Its Use in Molecular Dynamics Simulations of Collagen-Like Peptides", Sanghyun Park, Randall J. Radmer, Teri E. Klein, and Vijay S. Pande. Journal of Computational Chemistry (2005)
  • Direct calculation of the binding free energies of FKBP ligands using the Fujitsu BioServer massively parallel computer", Hideaki Fujutani, Yoshiaki Tanida, Masakatsu Ito, Guha Jayachandran, Christopher D. Snow, Michael R. Shirts, Eric J. Sorin, and Vijay S. Pande. Journal of Chemical Physics (2005)
  • "Error Analysis in Markovian State Models for protein folding", Nina Singhal and Vijay S. Pande. Journal of Chemical Physics (2005)
  • "How large is alpha-helix in solution? Studies of the radii of gyration of helical peptides by SAXS and MD", Bojan Zagrovic, Guha Jayachandran, Ian S. Millett, Sebastian Doniach and Vijay S. Pande. Journal of Molecular Biology (2005)
  • "Can conformational change be described by only a few normal modes?", Paula Petrone and Vijay S. Pande. Biophysical Journal (2005)

2006

  • "The solvation interface is a determining factor in peptide conformational preferences", Eric J. Sorin, Young Min Rhee, Michael R. Shirts, and Vijay S. Pande. Journal of Molecular Biology (2006)
  • "Nanotube confinement denatures protein helices", Eric J. Sorin and Vijay S. Pande. JACS (2006)
  • "On the role of chemical detail in simulating protein folding kinetics", Young Min Rhee and Vijay S. Pande. Journal of Chemical Physics (2006)
  • "Validation of Markov state models using Shannon's entropy", S. Park and V. S. Pande. Journal of Chemical Physics (2006)
  • "A novel approach for computational alanine scanning: application to the p53 oligomerization domain", L.T. Chong, W. C. Swope, J. W. Pitera, and V. S. Pande. Journal of Molecular Biology (2006)
  • "Electric Fields at the Active Site of an Enzyme: Direct Comparison of Experiment with Theory", Ian T. Suydam, Christopher D. Snow, Vijay S. Pande, Steven G. Boxer. Science (2006)
  • "Ensemble molecular dynamics yields submillisecond kinetics and intermediates of membrane fusion", P. Kasson, N. Kelley, N. Singhal, M. Vrjlic, A. Brunger, and V. S. Pande. Proceedings of the National Academy of Sciences, USA (2006)
  • "Folding Simulations of the Villin Headpiece in All-Atom Detail", Guha Jayachandran, V. Vishal, and V. S. Pande. Journal of Chemical Physics (2006)
  • "Parallelized Over Parts Computation of Absolute Binding Free Energy with Docking and Molecular Dynamics", Guha Jayachandran, M. R. Shirts, S. Park, and V. S. Pande. Journal of Chemical Physics (2006)
  • "Kinetic Definition of Protein Folding Transition State Ensembles and Reaction Coordinates", C. Snow and V. S. Pande. Biophysical Journal (2006)
  • "Local structure formation in simulations of two small proteins", Guha Jayachandran, V. Vishal, Angel E. Garcıa and V. S. Pande. Journal of Structural Biology (2006)
  • "A Bayesian Update Method for Adaptive Weighted Sampling", S. Park and V. S. Pande. Physical Review (2006)
  • "Predicting structure and dynamics of loosely-ordered protein complexes: influenza hemagglutinin fusion peptide", P. Kasson and V. S. Pande. PSB (2006)
  • "N-Body simulation on GPUs", Erich Elsen, Mike Houston, V. Vishal, Eric Darve, Pat Hanrahan, and Vijay Pande. Proceedings of the 2006 ACM/IEEE conference on Supercomputing (2006)

2007

  • "Storage@home: Petascale Distributed Storage", Adam L Beberg and Vijay S. Pande. IPDPS (2007)
  • "Automatic State Decomposition Algorithm", J. Chodera, N. Singhal, V. S. Pande, K. Dill, and W. Swope. Journal of Chemical Physics (2007)
  • "Protein folding under confinement: a role for solvent", D. Lucent, V. Vishal, V. S. Pande. Proceedings of the National Academy of Sciences, USA (2007)
  • "Persistent voids: a new structural metric for membrane fusion", P. M. Kasson, A. Zomorodian, S. Park, N. Singhal, L. J. Guibas, and V. S. Pande. Bioinformatics (2007)
  • "Control of Membrane Fusion Mechanism by Lipid Composition: Predictions from Ensemble Molecular Dynamics", P. M. Kasson and V. S. Pande. PLoS Computational Biology (2007)
  • "Heterogeneity Even at the Speed Limit of Folding: Large-scale Molecular Dynamics Study of a Fast-folding Variant of the Villin Headpiece", D. Ensign, P. M. Kasson, and V. S. Pande. Journal of Molecular Biology (2007)
  • "Calculation of the distribution of eigenvalues and eigenvectors in Markovian state models for molecular dynamics", Nina Singhal Hinrichs and Vijay S. Pande. Journal of Chemical Physics (2007)

2008

  • "Convergence of folding free energy landscapes via application of enhanced sampling methods in a distributed computing environment", Xuhui Huang, Gregory R. Bowman,and Vijay S. Pande. Journal of Chemical Physics (2008)
  • "Structural Insight into RNA Hairpin Folding Intermediates", Gregory R. Bowman, Xuhui Huang, Yuan Yao, Jian Sun, Gunnar Carlsson, Leonidas J. Guibas, and Vijay S. Pande. Journal of the American Chemical Society (2008)

High performance platforms

Graphical processing units

On October 2, 2006, the Folding@home Windows GPU client was released to the public as a 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 ATI X1900 GPUs, averaging at over 70x the performance of current CPU submissions, and the GPU clients remain the most powerful clients available in terms of performance per client (as of August 24, 2008, GPU clients accounted for the majority of entire project's throughput—over 1.8 petaFLOPs of computational power—at an approximate ratio of 9 clients per teraFLOP). On April 10, 2008, the second generation Windows GPU client was released to open beta testing, supporting ATI/AMD's HD2xxx and HD3xxx series, and also debuting a new core (GROGPU2 - Core 11). Inaccuracies with DirectX were cited as the main reason for the migration to the new version (the original GPU client was officially retired June 6, 2008), which uses AMD/ATI's CAL. On June 17, 2008, a version of the second-generation Windows GPU client for CUDA enabled Nvidia GPUs was also released for public beta testing. The GPU clients proved reliable enough to be promoted out of the beta phase and were officially released August 1, 2008.

PlayStation 3

Stanford announced in August 2006 that a folding client was available to run on the Sony PlayStation 3. 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 1.6 (released on Thursday, March 22, 2007) allows for Folding@home software, a 50 MB download, to be used on the PS3. 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 PS3s required to achieve a petaFLOPS level to around 60,000.

On April 26, 2007, Sony released a new version of Folding@home which improved folding performance drastically, such that the updated PS3 clients produced 1500 teraFLOPS with 52,000 clients versus the previous 400 teraFLOPS by around 24,000 clients. Lately, the console accounts for around 40% of all teraFLOPS at an approximate ratio of 35½ PS3 clients per teraFLOPS.

On December 19, 2007, Sony again updated the Folding@home client to version 1.3 to allow users to run music stored on their hard drives while contributing. Another feature of the 1.3 update allows users to automatically shut down their console after current work is done or after a limited period of time (for example 3 or 4 hours). Also, the software update added the Generalized Born implicit solvent model, so the FAH PS3 client gained more broad computing capabilities. Shortly afterward, 1.3.1 was released to solve a mishandling of protocol resulting in difficulties sending and receiving Work Units due to heavy server loads stemming from the fault.

On 18 September, 2008 the Folding@home client became Life With PlayStation. In addition to the existing functionality, the application also provides the user with access to information "channels", the first of which being the Live Channel which offers news headlines and weather through a 3D globe. The user can rotate and zoom in to any part of the world to access information provided by Google News and The Weather Channel, among other sources. This update also provided more advanced simulation of protein folding and a new ranking system.

Multi-core processing client

As more modern CPUs are being released, the migration to multiple cores is becoming more adopted by the public, and the Pande Group is adding symmetric multiprocessing (SMP) support to the Folding@home client in the hopes of capturing the additional processing power. The SMP support is being achieved by utilizing Message Passing Interface protocols. In current state it is being confined inside a single node by hard coded usage of the localhost.

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.

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. Some teams offer prizes in an attempt to increase participation in the project.

Development

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 Open-source software or under similar licenses.

A development version of Folding@home once ran on the open source BOINC framework; however, this version remained unreleased.

Estimated energy consumption

A PlayStation 3 has a maximum power rating of 380 watts. As Folding@home is a CPU intensive application, it causes 100% utilization. However, according to Stanford's PS3 FAQ, "We expect the PS3 to use about 200W while running Folding@home. As of March 30, 2008, there are 40,276 PS3s providing 1,216,000,000 MFlops of processing power. This amounts to 30,191MFlops/PS3, and with Stanford's estimate of 200W per PS3 (for original units manufactured on the 90nm process), 151MFlops/watt. This would put the PS3 portion of Folding@home at 34th on the February 2008 Green500 list The Cell processors used in current units of the PlayStation 3 utilize 65nm technology (lowering power consumption to around 115W per PS3), with another upgrade to 45nm planned (further dropping consumption to around 80W/PS3). This will further increase the power efficiency of the contribution from PlayStation 3 units.

The total power consumption required to produce the processing power required by the project can be estimated based upon the average FLOPS per watt. As of 2008, according to the Green500 list, the most efficient computer - also based on a version of the Cell BE - runs at 488.17 MFLOPS/watt. One petaFLOPS equals 1,000,000,000 MFLOPSs. Therefore, the current Folding@home project, if it were theoretically using the most efficient CPUs currently available, would use at least 2.8 megawatts of power per petaFLOPS, slightly more than the world's first and only petaflop system, the Cell-based Roadrunner which uses 2.345MW. This is equivalent to the power needed to light approximately 40,000 standard house light bulbs (between 60 and 100 watts each), or the equivalent of 0.5-3 electrical wind mills depending on their size.

Estimates of power usage per time period are more difficult than estimates of power usage per processing instruction. This is because Folding@home clients are often run on computers that would be powered-on even in the absence of the Folding@home client, and that run other programs simultaneously. While Folding@home increases processor utilization, and thus (usually) power consumption, the extent to which it does so is dependent on the client processor's normal operating load, and its ability to reduce clock speeds when presented with less-than-full utilization (a process known as dynamic frequency scaling). Consequently, the total power usage of the Folding@home client on a temporal basis is probably less than the figure that could be calculated by summing the peak power consumption of each of the project's component processors.

See also

References

Further reading

  • 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.
  • 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.
  • 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.
  • 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 313 (5784): 200 - 204.

External links

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