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), and to improve on the methods available to do so. It was launched on October 1, 2000, and is currently managed by the Pande Group, within Stanford University'schemistry department, under the supervision of Professor Vijay Pande.
Folding@home is the most powerful distributed computing cluster in the world, according to Guinness,[2] and one of the world's largest distributed computing projects.[3] The goal of the project is "to understand protein folding, misfolding, and related diseases."[4]
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.[5] 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 — which is a far longer simulation 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.[6] For that goal they collaborate with various scientific institutions.[7]
As of January 13, 2010, seventy-two scientific research papers have been published using the project's work.[8] A University of Illinois at Urbana-Champaign report dated October 22, 2002 states that Folding@home distributed simulations of protein folding are demonstrably accurate.[9]
Function
File:FAHMon.pngFolding@home when running takes advantage of unused CPU cycles on a computer system as shown by this computer's 99% 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 clientprogram. The client runs in the background, utilizing otherwise unused CPU power. The older, no longer used version of Folding@home for the CPU could be run as a screen saver, only folding 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.
Software
The Folding@home client consists of three separate components.
The client software acts as a download and file manager for work units and scientific cores, controls the cores, and is the software that the user interacts with. Separating the client from the core enables the scientific methods to be updated automatically (or new methods to be added) without a client update.
The Work Unit is the actual data that the client is being asked to process.
The Core performs the calculations on the work unit. Folding@homes cores are based on modified versions of seven molecular simulation programs for calculation: TINKER, GROMACS, AMBER, CPMD, SHARPEN, ProtoMol and Desmond.[10][11] 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):[12]
Active Cores
GROMACS (all variants of this core use SIMD optimizations including SSE, 3DNow+ or AltiVec, where available, unless otherwise specified)
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.
The Gromacs Serial Replica Exchange Method core, also known as GroST (Gromacs Serial replica exchange with Temperatures), uses the 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.
Currently inactive, in closed beta testing before general release.
Uses OpenCL to increase reliability and performance on GPUs.
May unify the ATI and nVidia GPU codebases (due to using OpenCL).
Will be available for GPU3 client only.
Participation
File:FAH-tflops.PNGFolding@home computing power shown - by device type - in TeraFLOPS as recorded semi-daily from November 2006 until September 2007. Note the large spike in total compute power after March 22, when the PlayStation 3 client was released.
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 ATigraphics cards and the PlayStation 3, and again following the launch of the high performance client for nVidia graphics cards.
As of April 9, 2009 the peak speed of the project overall has reached over 5.0 native PFLOPS (8.1 x86 PFLOPS[17]) from around 400,000 active machines, and the project has received computational results from over 4.51 million devices since it first started.[3]
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 about 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), it is available here (must access in IE with old version of Google Toolbar). 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.[18]
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.
PetaFLOPS milestones
Native petaFLOPS Barrier
Date Crossed
1.0
September 16, 2007
2.0
early May 2008
3.0
August 20, 2008
4.0
September 28, 2008
5.0
February 18, 2009
On September 16, 2007, the Folding@home project officially attained a sustained performance level higher than one native petaFLOPS, becoming the first computing system of any kind in the world to ever do so, although it had briefly peaked above one native petaFLOPS in March 2007, receiving a large amount of main stream media coverage for doing so.[19][20] In early May 2008 the project attained a sustained performance level higher than two native petaFLOPS, followed by the three and four native petaFLOPS milestones on August 20 and September 28, 2008 respectively. On February 18, 2009, Folding@home achieved a performance level of just above 5 petaFLOPS, thereby becoming the first computing system of any kind to surpass 5 native PFLOPS[21], just as it was for the other four milestones.
The Folding@home computing cluster currently operates at above 4.3 native petaFLOPS at all times, with a large majority of the performance coming from GPU and PlayStation 3 clients.[3] In comparison to this, the fastest standalone supercomputer (non-distributive computing) in the world (as of November 2008, U.S. Department of EnergyRoadrunner) peaks at approximately 1.46 petaFLOPS.[22]
Beginning in April 2009, Folding@Home began reporting performance in both "Native" FLOPS and x86 FLOPS.[3] ("x86" FLOPS reported at a much higher mark than the "Native" FLOPS) A detailed explanation of the difference between the two figures was given in the FLOP section of the Folding@Home FAQ.[17]
Results
These peer-reviewed papers (in chronological order) all use research from the Folding@home project.[8]
Michael R. Shirts and Vijay S. Pande (2001). "Mathematical Analysis of Coupled Parallel Simulations". Physical Review Letters86 (22): 4983–4987. doi:10.1103/PhysRevLett.86.4983.
Bojan Zagrovic, Eric J. Sorin and Vijay Pande (2001). "b-Hairpin Folding Simulations in Atomistic Detail Using an Implicit Solvent Model". Journal of Molecular Biology313: 151–169. doi:10.1006/jmbi.2001.5033.
2002
Stefan M. Larson, Christopher D. Snow, Michael R. Shirts, and Vijay S. Pande (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
Bojan Zagrovic, Christopher D. Snow, Michael R. Shirts, and Vijay S. Pande. (2002). "Simulation of Folding of a Small Alpha-helical Protein in Atomistic Detail using Worldwide distributed Computing". Journal of Molecular Biology323: 927–937. doi:10.1016/S0022-2836(02)00997-X.
Bojan Zagrovic, Christopher D. Snow, Siraj Khaliq, Michael R. Shirts, and Vijay S. Pande (2002). "Native-like Mean Structure in the Unfolded Ensemble of Small Proteins". Journal of Molecular Biology323: 153–164. doi:10.1016/S0022-2836(02)00888-4.
Christopher D. Snow, Bojan Zagrovic, and Vijay S. Pande (2002). "The Trp Cage: Folding Kinetics and Unfolded State Topology via Molecular Dynamics Simulations". Journal of the American Chemical Society124: 14548–14549. doi:10.1021/ja028604l.
2003
Vijay S. Pande, Ian Baker, Jarrod Chapman, Sidney P. Elmer, Siraj Khaliq, Stefan M. Larson, Young Min Rhee, Michael R. Shirts, Christopher D. Snow, Eric J. Sorin, Bojan Zagrovic (2003). "Atomistic protein folding simulations on the submillisecond timescale using worldwide distributed computing". Biopolymers68: 91–109. doi:10.1002/bip.10219.
Young Min Rhee & Vijay S. Pande (2003). "Multiplexed-Replica Exchange Molecular Dynamics Method for Protein Folding Simulation". Biophysical Journal84 (2): 775–786. doi:10.1016/S0006-3495(03)74897-8.
Eric J. Sorin, Young Min Rhee, Bradley J. Nakatani & Vijay S. Pande (2003). "Insights Into Nucleic Acid Conformational Dynamics from Massively Parallel Stochastic Simulations". Biophysical Journal85: 790–803. doi:10.1016/S0006-3495(03)74520-2.
Bojan Zagrovic and Vijay S. Pande (2003). "Solvent Viscosity Dependence of the Folding Rate of a Small Protein: Distributed Computing Study". Journal of Computational Chemistry24 (12): 1432–1436. doi:10.1002/jcc.10297.
Michael R. Shirts, Jed W. Pitera, William C. Swope, and Vijay S. Pande (2003). "Extremely precise free energy calculations of amino acid side chain analogs: Comparison of common molecular mechanics force fields for proteins". Journal of Chemical Physics119 (11): 5740–5761. doi:10.1063/1.1587119.
Michael R. Shirts, Eric Bair, Giles Hooker, and Vijay S Pande (2003). "Equilibrium Free Energies from Nonequilibrium Measurements Using Maximum-Likelihood Methods". Physical Review Letters91 (14). doi:10.1103/PhysRevLett.91.140601.
Bojan Zagrovic & Vijay S Pande (2003). "Structural correspondence between the alpha-helix and the random-flight chain resolves how unfolded proteins can have native-like properties". Nature Structural Biology10 (11): 955–961. doi:10.1038/nsb995.
2004
Eric J. Sorin, Bradley J. Nakatani, Young Min Rhee, Guha Jayachandran, V Vishal, & Vijay S Pande (2004). "Does Native State Topology Determine the RNA Folding Mechanism?". Journal of Molecular Biology337: 789–757. doi:10.1016/j.jmb.2004.02.024.
Christopher D. Snow, Linlin Qiu, Deguo Du, Feng Gai, Stephen J. Hagen, & Vijay S Pande (2004). "Trp zipper folding kinetics by molecular dynamics and temperature-jump spectroscopy". Proceedings of the National Academy of Sciences, USA101 (12): 4077–4082. doi:10.1073/pnas.0305260101.
Young Min Rhee, Eric J. Sorin, Guha Jayachandran, Erik Lindahl, & Vijay S Pande (2004). "Simulations of the role of water in the protein-folding mechanism". Proceedings of the National Academy of Sciences, USA101 (17): 6456–6461. doi:10.1073/pnas.0307898101.
Nina Singhal, Christopher D. Snow, and Vijay S. Pande (2004). "Using path sampling to build better Markovian state models: Predicting the folding rate and mechanism of a tryptophan zipper beta hairpin". Journal of Chemical Physics121: 415–425. doi:10.1063/1.1738647.
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 Biology345: 869–878. doi:10.1016/j.jmb.2004.10.083.
2005
Eric J. Sorin, Young Min Rhee, and Vijay S. Pande (2005). "Does Water Play a Structural Role in the Folding of Small Nucleic Acids?". Biophysical Journal88: 2516–2524. doi:10.1529/biophysj.104.055087.
Eric J. Sorin and Vijay S. Pande (2005). "Exploring the Helix-Coil Transition via All-atom Equilibrium Ensemble Simulations". Biophysical Journal88: 2472–2493. doi:10.1529/biophysj.104.051938.
Eric J. Sorin and Vijay S. Pande (2005). "Empirical Force-Field Assessment: The Interplay Between Backbone Torsions and Noncovalent Term Scaling". Journal of Computational Chemistry26: 682–690. doi:10.1002/jcc.20208.
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 Biophysics34: 43–69. doi:10.1146/annurev.biophys.34.040204.144447.
Bojan Zagrovic, Jan Lipfert, Eric J. Sorin, Ian S. Millett, Wilfred F. van Gunsteren, Sebastian Doniach & Vijay S. Pande (2005). "Unusual compactness of a polyproline type II structure". Proceedings of the National Academy of Sciences, USA102 (33): 11698–11703. doi:10.1073/pnas.0409693102.
Michael R. Shirts & Vijay S. Pande (2005). "Comparison of efficiency and bias of free energies computed by exponential averaging, the Bennett acceptance ratio, and thermodynamic integration". Journal of Chemical Physics122. doi:10.1063/1.1873592.
Michael R. Shirts & Vijay S. Pande (2005). "Solvation free energies of amino acid side chain analogs for common molecular mechanics water models". Journal of Chemical Physics122. doi:10.1063/1.1877132.
Sidney Elmer, Sanghyun Park, & Vijay S. Pande (2005). "Foldamer dynamics expressed via Markov state models. I. Explicit solvent molecular-dynamics simulations in acetonitrile, chloroform, methanol, and water". Journal of Chemical Physics123. doi:10.1063/1.2001648.
Sidney Elmer, Sanghyun Park, & Vijay S. Pande (2005). "Foldamer dynamics expressed via Markov state models. II. State space decomposition". Journal of Chemical Physics123. doi:10.1063/1.2008230.
Sanghyun Park, Randall J. Radmer, Teri E. Klein, and Vijay S. Pande (2005). "A New Set of Molecular Mechanics Parameters for Hydroxyproline and Its Use in Molecular Dynamics Simulations of Collagen-Like Peptides". Journal of Computational Chemistry26: 1612–1616. doi:10.1002/jcc.20301.
Hideaki Fujutani, Yoshiaki Tanida, Masakatsu Ito, Guha Jayachandran, Christopher D. Snow, Michael R. Shirts, Eric J. Sorin, and Vijay S. Pande (2005). "Direct calculation of the binding free energies of FKBP ligands using the Fujitsu BioServer massively parallel computer". Journal of Chemical Physics123. doi:10.1063/1.1999637.
Nina Singhal and Vijay S. Pande (2005). "Error Analysis and efficient sampling in Markovian State Models for protein folding". Journal of Chemical Physics123. doi:10.1063/1.2116947.
Bojan Zagrovic, Guha Jayachandran, Ian S. Millett, Sebastian Doniach and Vijay S. Pande (2005). "How large is alpha-helix in solution? Studies of the radii of gyration of helical peptides by SAXS and MD". Journal of Chemical Physics353: 232–241. doi:10.1016/j.jmb.2005.08.053.
2006
Paula Petrone and Vijay S. Pande (2006). "Can conformational change be described by only a few normal modes?". Biophysical Journal90: 1583–1593. doi:10.1529/biophysj.105.070045.
Eric J. Sorin, Young Min Rhee, Michael R. Shirts, and Vijay S. Pande (2006). "The solvation interface is a determining factor in peptide conformational preferences". Journal of Molecular Biology356: 248–256. doi:10.1016/j.jmb.2005.11.058.
Eric J. Sorin and Vijay S. Pande (2006). "Nanotube confinement denatures protein helices". Journal of the American Chemical Society128: 6316–6317. doi:10.1021/ja060917j.
Young Min Rhee and Vijay S. Pande (2006). "On the role of chemical detail in simulating protein folding kinetics". Chemical Physics323: 66–77. doi:10.1016/j.chemphys.2005.08.060.
L.T. Chong, W. C. Swope, J. W. Pitera, and V. S. Pande (2006). "A novel approach for computational alanine scanning: application to the p53 oligomerization domain". Journal of Molecular Biology357 (3): 1039–1049. doi:10.1016/j.jmb.2005.12.083.
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". Science313 (5784): 200–204. doi:10.1126/science.1127159.
P. Kasson, N. Kelley, N. Singhal, M. Vrjlic, A. Brunger, and V. S. Pande (2006). "Ensemble molecular dynamics yields submillisecond kinetics and intermediates of membrane fusion". Proceedings of the National Academy of Sciences, USA103 (32): 11916–11921. doi:10.1073/pnas.0601597103.
Guha Jayachandran, V. Vishal, and V. S. Pande (2006). "Folding Simulations of the Villin Headpiece in All-Atom Detail". Journal of Chemical Physics124. doi:10.1063/1.2186317.
Guha Jayachandran, M. R. Shirts, S. Park, and V. S. Pande (2006). "Parallelized Over Parts Computation of Absolute Binding Free Energy with Docking and Molecular Dynamics". Journal of Chemical Physics125. doi:10.1063/1.2221680.
C. Snow and V. S. Pande (2006). "Kinetic Definition of Protein Folding Transition State Ensembles and Reaction Coordinates". Biophysical Journal91: 14–24. doi:10.1529/biophysj.105.075689.
S. Park and V. S. Pande (2006). "A Bayesian Update Method for Adaptive Weighted Sampling". Physical Review74 (6). doi:10.1103/PhysRevE.74.066703.
P. Kasson and V. S. Pande (2006). "Predicting structure and dynamics of loosely-ordered protein complexes: influenza hemagglutinin fusion peptide". PSB. doi:10.1142/9789812772435_0005. PMID17992744.
Erich Elsen, Mike Houston, V. Vishal, Eric Darve, Pat Hanrahan, and Vijay Pande (2006). "N-Body simulation on GPUs". Proceedings of the 2006 ACM/IEEE conference on Supercomputing. doi:10.1145/1188455.1188649.
2007
Guha Jayachandran, V. Vishal, Angel E. Garcıa and V. S. Pande (2007). "Local structure formation in simulations of two small proteins". Journal of Structural Biology157 (3): 491–499. doi:10.1016/j.jsb.2006.10.001.
J. Chodera, N. Singhal, V. S. Pande, K. Dill, and W. Swope (2007). "Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics". Journal of Chemical Physics126 (15). PMID17461665.
D. Lucent, V. Vishal, V. S. Pande (2007). "Protein folding under confinement: a role for solvent". Proceedings of the National Academy of Sciences, USA104 (25): 10430–10434. doi:10.1073/pnas.0608256104.
P. M. Kasson, A. Zomorodian, S. Park, N. Singhal, L. J. Guibas, and V. S. Pande (2007). "Persistent voids: a new structural metric for membrane fusion". Bioinformatics. doi:10.1093/bioinformatics/btm250.
P. M. Kasson and V. S. Pande (2007). "Control of Membrane Fusion Mechanism by Lipid Composition: Predictions from Ensemble Molecular Dynamics". PLoS Computational Biology3 (11). doi:10.1371/journal.pcbi.0030220.
D. Ensign, P. M. Kasson, and V. S. Pande (2007). "Heterogeneity Even at the Speed Limit of Folding: Large-scale Molecular Dynamics Study of a Fast-folding Variant of the Villin Headpiece". Journal of Molecular Biology374 (3): 806–816. doi:10.1016/j.jmb.2007.09.069.
Alex Robertson, Edgar Luttmann, Vijay S. Pande (2007). "Effects of long-range electrostatic forces on simulated protein folding kinetics". Journal of Computational Chemistry29 (5): 694–700. doi:10.1002/jcc.20828.
Nina Singhal Hinrichs and Vijay S. Pande (2007). "Calculation of the distribution of eigenvalues and eigenvectors in Markovian state models for molecular dynamics". Journal of Chemical Physics126. doi:10.1063/1.2740261.
2008
Xuhui Huang, Gregory R. Bowman,and Vijay S. Pande (2008). "Convergence of folding free energy landscapes via application of enhanced sampling methods in a distributed computing environment". Journal of Chemical Physics128 (20). PMID18513049.
Gregory R. Bowman, Xuhui Huang, Yuan Yao, Jian Sun, Gunnar Carlsson, Leonidas J. Guibas, and Vijay S. Pande (2008). "Structural Insight into RNA Hairpin Folding Intermediates". Journal of the American Chemical Society130 (30): 9676–9678. doi:10.1021/ja8032857.
Nicholas W. Kelley, V. Vishal, Grant A. Krafft, and Vijay S. Pande. (2008). "Simulating oligomerization at experimental concentrations and long timescales: A Markov state model approach.". Journal of Chemical Physics129 (21). doi:10.1063/1.3010881.
Paula M. Petrone, Christopher D. Snow, Del Lucent, and Vijay S. Pande (2008). "Side-chain recognition and gating in the ribosome exit tunnel". Proceedings of the National Academy of Sciences, USA105 (43): 16549–16554. doi:10.1073/pnas.0801795105.
Edgar Luttmann, Daniel L. Ensign, Vishal Vaidyanathan, Mike Houston, Noam Rimon, Jeppe Øland, Guha Jayachandran, Mark Friedrichs, Vijay S. Pande (2008). "Accelerating Molecular Dynamic Simulation on the Cell processor and PlayStation 3". Journal of Computational Chemistry30 (2): 268–274. doi:10.1002/jcc.21054.
2009
Peter M. Kasson and Vijay S. Pande (2009). "Combining Mutual Information with Structural Analysis to Screen for Functionally Important Residues in Influenza Hemagglutinin". Pacific Symposium on Biocomputing14: 492–503. PMID19209725.
Nicholas W. Kelley, Xuhui Huang, Stephen Tam, Christoph Spiess, Judith Frydman and Vijay S. Pande (2009). "The predicted structure of the headpiece of the Huntingtin protein and its implications on Huntingtin aggregation". Journal of Molecular Biology. doi:10.1016/j.jmb.2009.01.032.
M. S. Friedrichs, P. Eastman, V. Vaidyanathan, M. Houston, S. LeGrand, A. L. Beberg, D. L. Ensign, C. M. Bruns, V. S. Pande (2009). "Accelerating molecular dynamic simulation on graphics processing units". Journal of Computational Chemistry. doi:10.1002/jcc.21209. PMID19191337.
D. L. Ensign and V. S. Pande (2009). "The Fip35 WW Domain Folds with Structural and Mechanistic Heterogeneity in Molecular Dynamics Simulations". Biophysical Journal96 (8): L53-55. PMID19383445.
V. A. Voelz, E. Luttmann, G. R. Bowman, and V.S. Pande (2009). "Probing the nanosecond dynamics of a designed three-stranded beta-sheet with massively parallel molecular dynamics simulation". International Journal of Molecular Sciences10 (3): 1013. doi:10.3390/ijms10031013.
A. Beberg and V. S. Pande (2009). "Folding@home: lessons from eight years of distributed computing". IEEE International Parallel and Distributed Processing Symposium: 1-8. doi:10.1109/IPDPS.2009.5160922.
G. R. Bowman, X. Huang, and V. S. Pande (2009). "Using generalized ensemble simulations and Markov state models to identify conformational states". Methods49 (2): 197-201. doi:10.1016/j.ymeth.2009.04.013.
G. R. Bowman and V. S. Pande (2009). "The Roles of Entropy and Kinetics in Structure Prediction". PLoS One4 (6): e5840. doi:10.1371/journal.pone.0005840.
Peter M. Kasson, Daniel L. Ensign and Vijay S. Pande (2009). "Combining Molecular Dynamics with Bayesian Analysis To Predict and Evaluate Ligand-Binding Mutations in Influenza Hemagglutinin". Journal of the American Chemical Society. PMID19637916.
S. Bacallado, J. Chodera, and V. Pande (2009). "Bayesian comparison of Markov models of molecular dynamics with detailed balance constraint". Journal of Chemical Physics131. doi:10.1063/1.3192309.
2010
Vincent A. Voelz, Gregory R. Bowman, Kyle Beauchamp and Vijay S. Pande (2010). "Molecular Simulation of ab Initio Protein Folding for a Millisecond Folder NTL9(1−39)". Journal of the American Chemical Society. doi:10.1021/ja9090353.
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 Radeon X1900 GPUs, averaging at over 70x the performance of current CPU submissions, and the GPU clients remain the most powerful clients available in performance per client (as of March 11, 2009, GPU clients accounted for over 60% of the entire project's throughput at an approximate ratio of 9 clients per teraFLOP).[3]
On April 10, 2008, the second generation Windows GPU client was released to open beta testing, supporting ATI/AMD's Radeon HD 2000 and HD 3000 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[23]), 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.[24] The GPU clients proved reliable enough to be promoted out of the beta phase and were officially released August 1, 2008.[25] Newer GPU cores continue to be released for both CAL and CUDA.
While the only officially released GPU v2.0 client is for Windows, this client can be run on Linux under Wine with NVIDIA graphics cards.[26] The client can operate on both 32- and 64-bit Linux platforms, but in either case the 32-bit CUDA toolkit is required. This configuration is not officially supported, though initial results have shown comparable performance to that of the native client and no problems with the scientific results have been found[citation needed]. An unofficial installation guide has been published.[26]
On September 25, 2009, Vijay Pande revealed in his blog that a new third version of the GPU client was in development.[27] GPU3 will use OpenCL (preferred over DirectX 11's Compute Shaders) as the software interface, which may mean that the GPU core will be unified for both ATI and nVidia, and may also mean the addition of support for other platforms with OpenCL support.
PlayStation 3
File:LifeWithPlayStation Folding.jpgThe PlayStation 3's Life With PlayStation client replaced the Folding@home application on September 18, 2008.
Stanford announced in August 2006 that a folding client was available to run on the Sony PlayStation 3.[28] 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.[3]
A peak output of the project at 990 teraFLOPS was achieved on March 25, 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.[29] Lately, the console accounts for around 26% 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.[30][31]
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 September 18, 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, all running whilst folding in the background. This update also provided more advanced simulation of protein folding and a new ranking system.[32]
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 (MPI) 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 x86-64Linux and x86 Mac OS X were released. The beta win32 SMP Folding@home client is out as well, and a 32-bit Linux client is currently in development.[33]
On June 17, 2009 the Pande Group revealed that a second generation SMP client (known as the SMP2 client) was in development. This client will use threads rather than MPI[11] to spread the processing load across multiple cores and thereby remove the overhead of keeping the cores synced, as they should share a common data bank in RAM. On January 24th, 2010, the first open beta release of the SMP2 client was made, trialling the new processing methods and a new points bonus system rewarding quick unit returns.
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.[34] Some teams offer prizes in an attempt to increase participation in the project.[35][36]
Development
The Folding@home project does not make the project source code available to the public, citing security and integrity concerns.[37][38] 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.[39]
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."[40]
As of December 27, 2008, there are 55,291 PS3s providing 1,559,000,000 MFlops of processing power. This amounts to 28,196 MFlops/PS3, and with Stanford's estimate of 200W per PS3 (for original units manufactured on the 90 nm process), 140.98 MFlops/watt.[3] This would put the PS3 portion of Folding@home at 95th on the November 2008 Green500 list.[41]
The Cell processors used in 65 nm PlayStation 3s lower power consumption to around 140W per PS3, whilst the 45 nm PS3s reduce this again to around 100W. This further increases 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 November 2008, according to the Green500 list, the most efficient computer - also based on a version of the Cell BE - runs at 536.24 MFLOPS/watt.[42] 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 1-3 wind turbines depending on their size.[43]
Estimates of energy usage per time period are more difficult than estimates of energy 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.
Also, in a Folding@home client that is run on a home computer where a heating system is being used, the excess heat generated by the power usage would theoretically reduce the amount of energy needed to heat the building.[citation needed] However, any such energy gains would be more than offset by the need for cooling during the warmer months.
Anticipated hardware performance
In 2009 the hardware performance database using the Google sites and docs platform was established.[44] This allows users to add their performance data to a database that can keep track of different variables. This is designed to be an aid to new users who are curious as to how well their system is performing compared to others.[44]
↑C. Snow, H. Nguyen, V. S. Pande, and M. Gruebele. (2002). "Absolute comparison of simulated and experimental protein-folding dynamics". Nature420 (6911): 102–106. doi:10.1038/nature01160. PMID12422224.
