Folding@home

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Folding@home
F@H Logo 2012.png
Original author(s) Vijay Pande
Developer(s) Stanford University / Pande lab
Initial release 2000-10-01
Stable release

Windows:
   Uniprocessor: 6.23[1]
   Nvidia GPU3: 6.41[2]
   ATI GPU2: 6.41[2]
Mac OS X:
   Uniprocessor/SMP: 6.29.3[1]
Linux:
   SMP: 6.34[1][2]

PlayStation 3:
   Life with PlayStation: 1.4[3]
Preview release Windows, Mac, and Linux:
7.1.48[4] / February 15, 2012; 12 days ago (2012-02-15)[5]
Operating system Microsoft Windows, Mac OS X, Linux
Platform Cross-platform
Available in English
Type Distributed computing
License Proprietary[6]
Website folding.stanford.edu

Folding@home is a distributed computing project designed to use spare processing power on personal computers to perform simulations of disease-relevant protein folding and other molecular dynamics, and to improve on the methods of doing so. Also referred to as FAH or F@h, much of its work attempts to determine how proteins reach their final structure, which is of significant academic interest and has major implications to disease research. To a lesser degree Folding@home also tries to predict that final structure from only the initial amino acid sequence, which has applications in drug design.[7][8] Folding@home is developed and operated by the Pande Laboratory at Stanford University, under the direction of Vijay Pande. The goal of the project is to "understand protein folding, misfolding, and related diseases".[9][10]

Folding@home's simulations of protein folding and misfolding enable the scientific community to better understand the development of many diseases, including Alzheimer's disease, Parkinson's disease, cancer, Creutzfeldt–Jakob disease, Huntington's disease, cystic fibrosis, sickle-cell anaemia, HIV, Chagas disease, influenza, osteogenesis imperfecta, autism,[11] and alpha 1-antitrypsin deficiency, among others.[12] More fundamentally, understanding the process of protein folding — how proteins assemble themselves into a functional state — is one of the outstanding problems of molecular biology.[13] Since the project's launch on October 1st 2000,[9] the Pande lab has produced ninety-five scientific research papers as a direct result of the project using simulation methodology that is a paradigm shift away from traditional computational approaches.[14][15] These techniques have demonstrated accuracy compared to results from laboratory research, a "grand challenge" in computational biology.

Folding@home pioneered the uses of GPUs, PlayStation 3s, and Message Passing Interface (used for computing on multi-core processors) for distributed computing. In January 2010 the Folding@home project successfully simulated protein folding in the 1.5 millisecond range — a simulation thousands of times longer than previously achieved. It remains one of the world's fastest computing systems, and is more powerful than all distributed computing projects under BOINC combined. The Pande lab collaborates with and shares this resource with various scientific institutions and laboratories across the world.[16]

Contents

[edit] Biomedical significance

Further information: Protein folding
Illustration of a protein before and after folding

Proteins are an essential component to many biological functions, and participate in virtually every process within cells. They often act as enzymes, performing biochemical reactions including cell signaling, transportation, cellular regulation, and others. As structural elements, some proteins act as a type of skeleton for cells, and as antibodies other proteins help the immune system. Before a protein can take on these roles, it must fold into a functional three-dimensional structure, a processes that often occurs spontaneously and is strongly dependent on the protein's amino acid sequence. Understanding protein folding is thus critical to understanding what a protein does and how it works. However, proteins may misfold — that is, it folds down the wrong pathway and end up misshapen — and unless cellular mechanisms are capable of destroying or refolding them, they can subsequently aggregate and cause diseases. The tendency for this to occur is heavily dependent on the protein's chemical properties and the surrounding cellular environment, and laboratory experiments studying this process can be severely limited in scope and detail. This has led to the development of physics-based computational models that, when complementing experiments, seek to provide a more complete picture of protein folding, misfolding, and aggregation.[17][18][19][20]

Since the 1990s molecular dynamics simulations have been severely limited by computational power.[20] In 2001 simulations could only achieve nanosecond to single microsecond timescales, while experiments revealed millisecond folding events.[8][18] Due to the complexity of the protein's conformation space and the timescales over which it folds, accurately simulating protein folding is significantly beyond the capabilities of a single modern computer and is considered a "holy grail" of computational biology.[21][22][23] Supercomputers have attempted to successfully to address this problem, but are intrinsically expensive, typically shared between hundreds of different research groups, and strong molecular simulation scaling is difficult.[24] Without the use of customized hardware, straightforward computations on biologically and experimentally relevant timescales typically have exceptional difficulty for all but the most elementary of systems, which has prompted the use of simplified models which may be insufficient for a comprehensive view of protein folding.[18][25] Moreover, a limited number of long simulations are not sufficient for revealing the protein dynamics because protein folding is intrinsically statistical.[18]

Folding@home simulation techniques rely on the behavior of proteins to spend a significant amount of the folding time "waiting" in various unique conformational states, each a free energy minima, before quickly transitioning to the next configuration.[24] Based on a small set of initial data, (from previous simulations or experiments) the Pande lab can identify and organize thousands of these states and build Markov state models. These MSMs use short simulations to find the statistical chances and rates of transitions between these states and essentially serves as a map of the protein's free energy landscape and kinetic and equilibrium thermodynamics properties.[18][20][24] Not only can this information be determined in parallel for each transition, but the transitions themselves can also be individually computed, and the overall protein folding process can then be reconstructed. This parallelization allows for a significant reduction in serial calculation time. Each MSM is also capable of revealing which transitions are limiting the accuracy of the model, which allow for specific follow-up simulations to efficiently improve the thoroughness of the model. Using this adaptive sampling technique, the amount of time it takes to construct an accurate Markov State Model is inversely proportional to the number of parallel simulations run. This statistical kinetic model can be viewed at an arbitrary resolution, reveals measurement uncertainty and diversity of the folding pathways, (including how the protein can misfold) allows simulations at biologically relevant timescales, and has accurately compared to experiments.[15][18][24] In 2010, the Pande lab used Markov state models to simulate the millisecond-folder NTL9 protein, a thousand times longer than previously achieved, even though each individual trajectories run on Folding@home were two orders of magnitude shorter.[15][26] This was the first demonstration that MSMs are capable of statistically capturing folding events that could not be seen by conventional simulation methods.[18] For the instrumental development of the software used to automatically build these MSMs and for attaining quantitative agreement between theory and experiment, in 2010 Folding@home researcher Greg Bowman was awarded the Thomas Kuhn Paradigm Shift Award from the American Chemical Society.[27][28]

The Pande lab and other researchers can use Folding@home to study aspects of folding, misfolding, and related diseases that would never be seen experimentally.[29] For example, most proteins have such intrinsically stable native states that it is difficult to experimentally study their folding events, and researchers must resort to chemical denaturation methods.[30] While the denatured state of proteins is of scientific interest because any residual structure may affect its folding behavior, even with molecular experiments it remains difficult to determine the extent of a local structure that may be present. However, molecular simulations on Folding@home can provide detail into denatured conformational states and insights into the chemical denaturation mechanisms.[30] These and other simulations run on Folding@home are used in conjunction with laboratory experiments.[29] Additionally, as of 2011 the Pande lab is performing studies into how protein folding in their native cells may be different than in environments such as test tubes used during experiments.[31] Protein folding often occurs in crowded cellular environments, such as in the ribosome's exit tunnel or inside protein chaperones.[32][33]

In addition to the diseases listed below, researchers have used Folding@home to study malaria, Chagas disease, the misfolding of alpha-synuclein that is linked to Parkinson's disease, the amylin peptide involved in type II diabetes,[34] and the prions which cause Creutzfeldt–Jakob disease.[12][35] Results from this research have led to major shifts in the understanding of protein folding and its applications for disease, as well as improved protein folding models.[36][37] Folding@home is dedicated to producing significant amounts of results towards protein folding, the diseases that result from protein misfolding, and novel computational methods for doing so.[12] The goal of the first five years of the project was to make significant advances in understanding folding, while the current goal is to understand misfolding and related disease, especially Alzheimer's disease.[29] In 2002, Folding@home completed approximately a million CPU days of simulations over the span of several months.[38]

As a part of Stanford University, a non-profit organization, the Pande lab does not sell the results generated by Folding@home. The large data sets from the project are freely available for other researchers to use upon request, and some can be accessed from the Folding@home website.[39][40] The Pande lab also releases Folding@home's key software to other researchers, so that the algorithms which benefit Folding@home will also aid other scientific areas.[39] Moreover, in 2011 they released the open-source Copernicus software, so that other researchers can run molecular simulations much more efficiently on clusters or supercomputers.[41] Summaries of all of the scientific findings from Folding@home are posted on the Folding@home website after publication.[14] The full publications are available online or from a local municipal or academic library.[40]

[edit] Alzheimer's disease

Enzymes act on the amyloid precursor protein and cut it into Aß fragments, which then aggregate to form senile plaques; characteristic of Alzheimer's patients.

Alzheimer's disease, a form of dementia which most often affects the elderly, is believed to be caused by specific misfolding and subsequent aggregation of the small 42-residue amyloid beta (Aß) peptide. The severity of the disease depends not only on the amount of Aß, but also on how it misfolds. Current theory holds that toxic non-plaque Aß oligomers (aggregates of many monomers) bind to a surface receptor on neurons and change the structure of the synapse, thereby disrupting neuronal communication and causing neuronal cell death which leads to the associated neurodegenerative consequences.[42] In 2011, Folding@home completed simulations of several mutant forms of this peptide, the results of which could aid in the development of therapeutic approaches to AD,[43] and will also help the Pande lab prepare for similar aggregation studies.[44]

Despite this connection to the disease, toxic Aß aggregations remain so complex that it was not previously possible to simulate them in atomic resolution. In 2011, the Pande lab explored how their Abeta studies using Folding@home could be used as a starting point for a new Alzheimer's therapy. Folding@home is currently concentrating on Alzheimer's and continues full-scale simulations of amyloid beta and its oligomerization,[45][46] which had previously been a technological challenge to simulate.[44] These studies build off of the Pande lab's 2008 published work into new ways to simulate Abeta oligomerization over long timescales. In the same publication, previous all-atom simulations were performed that led to specific experimentally-tested predictions, such as ways in which to stabilize the protein and prevent the toxic oligomer formation. The Pande lab is focusing their research in this area for rational drug design approaches.[12] Pande described that paper as the "tip of the iceberg" for the Folding@home studies of Alzheimer's, as further results will follow and possibly new therapeutics.[44][45]

Folding@home is also being used to study Aß fragments of different sizes to determine how various natural enzymes affect the structure and folding of Aß. These fragments are tied to senile plaques, a pathological marker of Alzheimer's disease in patient's brain. When certain enzymes cleave the amyloid precursor protein, Abeta peptides are produced, while the action of other enzymes can instead produce p3 peptides, much smaller fragments of Aß. Folding@home is simulating one of these smaller peptides in water in an effort to determine how the length of Aß affects its overall structure.[47]

In 2010, several possible drug leads predicted by Folding@home went from the test tube to testing on living tissue, and in close cooperation with the Nanomedicine Center for Protein Folding, the drug leads continued to be refined. Additionally, as predicted by FAH's simulations, a stable form of amyloid beta was experimentally verified which the Pande lab believes could be used as a starting point for new Alzheimer's therapy.[44][45] In 2008, Folding@home produced several small drug candidates to fight Alzheimer's Disease, as they appear to inhibit the toxicity of Abeta.[48]

The Pande lab is also using Folding@home to investigate protein–protein interactions, which occur extensively throughout both benign and disease-related biological activities. Interactions involving the common SH3 protein are also being studied, as it has implications in Alzheimers research. The refinement of these simulations has greatly improved the Pande lab's ability to understand a wide variety of biological interactions.[49]

[edit] Huntington's disease

Huntington's disease, an incurable neurodegenerative genetic disorder affecting muscle coordination and leading to dementia, is also associated with protein misfolding. Specifically, it is caused by a mutation in the Huntingtin gene, which causes excessively long repetitive chains of the glutamine amino acid in the Huntingtin protein, a protein that plays important roles in nerve cells.[50][51] The likelihood of neuronal cell death is primarily affected by the length of the glutamine chain and the neuron's intracellular exposure to the misfolded Huntingtin protein.[52] The defective protein causes Huntington's by aggregating most often in the striatum and frontal cortex of patient's brains. The Pande lab is using Folding@home to study these aggregates, as well as predict how they form.[12] How this aggregation occurs has been largely unknown, but in 2009 a paper based on Folding@home's results and published in the Journal of Molecular Biology investigated possible mechanisms for the aggregation formation, and the implications into how to prevent it.[51] These studies will be useful for drug design approaches against the disease, and will serve as a foundation for methods to stop the aggregation formation. Additionally, some of the methods used to study Huntington's are also being used for Alzheimer's research.[12]

In 2010, Folding@home researcher Veena Thomas proposed a novel therapeutic strategy for HD, which may be funded by the NIH. This strategy could be used to bring the results from Folding@home directly to a therapeutic.[12]

[edit] Cancer

More than half of all known cancers involve mutations of p53, a tumor suppressor protein present in every cell which signals for cell death in the event of damage to a cell's DNA. Specific mutations can cause p53 to misfold and become unable to send the "stop signal", consequently causing unchecked cell division. The type of mutation differs among the different locations of cancer, and analysis of these mutations is important for understanding the cause of these tumors.[53] In 2006 and in agreement with experimental data, the Pande lab's computational methods demonstrated reasonable success in reliably and rapidly identifying specific mutations that are linked to cancer. Moreover, the study was able to determine the effects of mutations that could not be experimentally measured. Further simulations followed of all possible amino acid possibilities at each sequence position, in order to thoroughly predict cancer-associated mutations.[54] This followed work in 2005 which studied the dimerization of the p53 oligomerization domain, was the first study to examine the refolding of a protein dimer using molecular simulations in explicit water, and was the first peer-reviewed publication from a distributed computing project related to cancer.[55][56] Following these results, the Pande lab expanded their efforts to other p53-related diseases.[12]

The Pande lab is also performing research into protein chaperones.[57] High temperatures and crowded cellular environments create stress on intracellular molecular processes, so these proteins assist in folding, assembly of oligomeric structures, the prevention of potential damage caused by protein misfolding, and other functions.[58][59] They are needed for these purposes by rapidly growing cancerous cells. Using Folding@home and working closely with the Protein Folding Center, the Pande lab plans to find ways to inhibit chaperones involved in cancerous cells. Using Folding@home for a more comprehensive visualization of their functions, the Pande lab and the Protein Folding Center collectively plan to engineer modified chaperonins to inhibit the folding of particular proteins associated with human diseases such as cancer and Alzheimer's. While this approach has been tried before, they believe that this project, if successful, could lead to an interesting new drug against cancer or at least make major advances in that area.[57]

Folding@home is also used to study the folding of several other proteins which have mutations tied to cancer, such as the enzyme src Kinase and certain forms of the Engrailed homeodomain. These proteins also have a great deal of experimental data for comparison, and serve as a great system for the understanding of folding and misfolding.[60][61] Additionally, the Pande lab is using Folding@home to understand the dynamics of a small knottin protein and how it can be used to bind to contrast agents for imaging scan or drugs.[62] Finally, some forms of interleukin-2, an important signaling protein for the immune system, have been used as immunotherapy for cancer. The Pande lab believes that Folding@home's simulations of its dynamics will lead to insights into how to design other therapeutics.[63]

[edit] Osteogenesis imperfecta

Osteogenesis imperfecta is a non-curable genetic bone disorder. Those with the disease are unable to successfully make functional connective bone tissue. This is lethal for many but can also induce a higher rate of miscarriages.[12] The disease is caused by mutations in the Type-1 collagen protein, the most common form of collagen and found abundantly throughout the body. Although some of these mutations of collagen can lead to serious morphological disorders, more benign forms can cause brittle bones and other subtleties.[12] Folding@home has performed simulations of collagen, and has produced a paper on Osteogenesis imperfecta outlining new molecular simulation techniques and revealing new insights into how collagen misfolds. The Pande lab believes these results will be useful for later computational studies of collagen.[64]

[edit] Viruses

Folding@home is also performing simulations of certain viruses such as influenza and HIV. These viruses are widespread, have a history of causing global pandemics, and are exceptionally difficult to treat. While the majority of treatments focus on preventing viral replication, Folding@home's research concentrates on preventing the virus from entering cellular membranes in the first place.[12] Membrane fusion is a primary action that occurs during this viral entry, and an understanding of this process has implications for antiviral drugs and national health. Unfortunately, membrane mechanisms are difficult to analyse experimentally. Molecular dynamics simulations run on Folding@home can simulate the protein-lipid interactions involved during cellular viral infection, which have improved the Pande lab's understanding of the molecular dynamics behind this process.[12][65] Research using Folding@home also continues on the dynamics of the enzyme RNase H, a key component of HIV, in the hopes of designing drugs to deactivate it.[66]

[edit] Drug design

Once it is determined how a protein misfolds, preventative treatments can be applied during the folding process.[20] The combination of computational molecular modeling and experimental analysis has the possibility to fundamentally shape the future of molecular medicine and the rational design of therapeutics.[19] Computational drug design has two components; protein docking and free energy calculation. These are difficult and time-consuming to determine experimentally and traditional computational approaches must usually trade speed for accuracy.[67] Moreover, free energy calculation of molecular processes is a challenge in computational chemistry, but is pharmaceutically useful.[68][69] In 2006 the Pande lab presented methods for FAH which parallelized these calculations, which increased significantly increased computational efficiency compared to previous techniques. These methods had implications in docking, molecular dynamics, and free energy perturbation,[68] and allowed Folding@home to perform drug design calculations that were otherwise infeasible.[29][69]

Scientists can utilize Folding@home to precisely demonstrate how potential drugs will bind to proteins,[67] which is important because a ligand that strongly binds to a target protein is a promising drug candidate.[69] In 2011 as part of a blind experiment, Folding@home attempted to predict which of a set of ligands would attach to a target protein and also estimate their associated binding energies.[70][71] As of 2011, Folding@home is searching for prime binding locations on protein surfaces by testing the interactions of different molecules with known binding sites.[72] Additionally, studies are also underway on the dynamics of beta-lactamase, a protein that plays important roles in drug resistance. Following the results from this study, the Pande lab may be able to design drugs to deactivate it.[73]

As part of the drug design efforts, Folding@home also simulates the ribosome, a large biological machine that synthesizes proteins from mRNA. It is targeted by approximately half of all known antibiotics, which usually kill bacteria by preventing their ribosomes from making new and essential proteins. The full structure of the ribosome has only been recently determined, but the functions of many ribosomal proteins remain largely unknown.[74] In 2007 the Pande lab received a grant to study and design new antibiotics. The size and complexity of the ribosome has prepared the Pande lab for more complex biomedical problems.[12]

[edit] Participation

Folding@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.

Interest and participation in the project has grown steadily since its launch.[75][76] As of February 24, 2012, Folding@home has about 356,000 active CPUs, about 32,000 active GPUs, and about 26,000 active PS3s, for a total of about 5.9 native petaFLOPS, (8.4 x86 petaFLOPS) more computing power than the combined efforts of all distributed computing projects under BOINC.[77][78] A large majority of this performance comes from the GPU and PS3 clients.[77] With the Markov state model approach, Folding@home achieves strong scaling across its user base; it gains a near-linear speedup for every additional processor.[24][79] In 2007, Guinness recognized Folding@home as the most powerful distributed computing cluster in the world.[80] This large and powerful network allows FAH to do work not possible any other way, including through the use of supercomputers,[81] which are typically expensive to operate and often shared.[82][24]

Folding@home gained popularity early in its history. In March 2002, Google co-founder Sergey Brin launched Google Compute as add-on for the Google Toolbar.[83] Although limited in functionality and scope, it increased Folding@home's participation from 10,000 up to about 30,000 active CPUs.[84] The program ended in October 2005 in favor of the Pande lab's official clients, and is no longer available for the Toolbar.[85][86] Folding@home also gained participants from Genome@home, another distributed computing project from the Pande lab and a sister project to Folding@home. The goal of Genome@home was protein design and its applications, and the project was officially concluded in March 2004. Following its completion, users were asked to donate to Folding@home instead.[87][88]

[edit] PetaFLOPS milestones

Native petaFLOPS threshold Date crossed Fastest Supercomputer at Date CrossedNote 1
1.0 September 16, 2007 0.2806 petaFLOP BlueGene/L[89]
2.0 May 7, 2008 0.4782 petaFLOP BlueGene/L[90]
3.0 August 20, 2008 1.042 petaFLOP Roadrunner[91]
4.0 September 28, 2008 1.042 petaFLOP Roadrunner[91]
5.0 February 18, 2009 1.105 petaFLOP Roadrunner[92]
6.0 November 10, 2011 8.162 petaFLOP K computer[93]

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 do so,[94][95] although it had erroneously almost reached that level in March of that year.[96][97] On May 7, 2008, the project attained a sustained performance level higher than two native petaFLOPS,[98] followed by the three and four native petaFLOPS milestones on August 20 and September 28, 2008 respectively.[99][100] Then on February 18, 2009, Folding@home achieved a performance level of just above five native petaFLOPS, thereby becoming the first computing system to surpass that performance, just as it was for the other four milestones.[101][102] Most recently, on November 10, 2011, Folding@home crossed the six native petaFLOP barrier with the equivalent of nearly eight x86 petaFLOPS.[103]

Starting in March 2009, Folding@home began reporting performance in both native and x86 FLOPS.[104] While native FLOPS are a measure of the performance from a given hardware, Folding@home also estimates how many FLOPS the calculation would take on the standard x86 CPU architecture, which is commonly used as a performance reference. For instance, certain complex functions can be performed in one native FLOP on a GPU, but take multiple FLOPS on the x86 architecture.[105] Despite using conservative conversions based on the actual execution time of the calculations, for the GPU and PS3 clients x86 FLOPS are consistently much greater than the native FLOPS.[77][106] By reporting in both native and x86 FLOPS, Folding@home attempts to even out these hardware differences.[105]

[edit] Points

Distributed computing projects such as Folding@home are often driven by a sense of collegiate competition to compute the most for the project. Folding@home quantitatively assesses this through a point system.[107][108] Donors are granted point credit as a measure of their contribution, and these points can foster friendly competition between donors.[82] Points are determined by the performance of each contributor's folding hardware relative to a reference machine, and one or more Work Units from a project are benchmarked on that machine before the project is released. As some simulations are exceptionally demanding on a system, or are of great scientific priority, donors who opt-in and reliably complete these Work Units are non-linearly rewarded additional bonus points.[108][109] This generates a fair system of equal pay for equal work, and attempts to align credit with the value of the scientific results.[107][110] Donors can also use a passkey to securely protect their contributions, as they not only allow for the receipt of bonus points, but they also separate a donor from any policy issues arising from another using that username.[111]

Users can register their contributions under a team, which register the combined score of all their members. A user can start their own team, or they can join an existing team.[1][112] They can be used for troubleshooting or recruitment purposes, but can also keep donors motivated.[113] In some cases, a team may have their own community-driven sources of help such as a forum.[114][115] In addition, rivalries between teams create friendly competition that benefits the folding community,[116] and members can also have intra-team competitions for top spots.[117][118] However, regardless of username or team affiliation, all contributions go to the same place and have the same scientific value.[114] Rankings and other statistics for both individuals and teams are posted to the Folding@home website, with third party statistics sites also available.[119]

[edit] Software

Folding@home software on the user's end consists of three components: a client, work units, and cores.

[edit] Client

Folding@home participants install client programs on their personal computer or on the PlayStation 3 gaming console. Computer clients tailor to multi-core processors and graphics processing units, complementing the standard client designed for uniprocessor systems. While these former clients use significantly more resources, the diversity and power of each hardware architecture provides Folding@home with the ability to efficiently complete many different types of simulations in a timely manner, (in a few weeks or months rather than years) which is of significant scientific value. Together, these clients allow the Pande lab to address biomedical questions previously considered impossible to tackle computationally.[29][120]

Each client is the software with which the user interacts, and manages the other software components behind the scenes. Through the client, the user may pause the folding process, open an event log, check the work progress, or view personal statistics.[121] They run continuously in the background at an extremely low priority using otherwise unused processing power, but will give processing precedence to other applications.[122][123] Although modern computer chips contain thermal protection and other safety features, the maximum CPU utilization can also be manually adjusted if desired.[121][124] Folding@home's clients also periodically create calculation checkpoints so that if they are interrupted they can resume work at almost the same point at startup.[125] For users with machines with multiple processor units, multiple clients may be installed on one machine, and users may be credited by clients on multiple machines.[122]

For security and scientific integrity reasons, the Pande lab does not publicly release the source code of the clients.[6][126] Significant work goes into minimizing security issues in all of Folding@home's software.[122][127] For example, clients can be downloaded only from the official Folding@home website or its commercial partners.[6] It will upload and download data only from Stanford's Folding@home data servers, (over port 8080, with 80 as an alternative)[128] and will only interact with FAH computer files.[122][127] Moreover, it does not normally need computer administrative privileges,[127] so from a security standpoint it behaves similar to but is even more secure than a web browser.[84][128]

Folding@home's first client was a screensaver, which would run Folding@home while the computer was not otherwise in use.[129] In January 2006 the Pande lab tested FAH clients on the open source BOINC framework; however, this approach became unworkable and was abandoned in June of that year.[130] BOINC's fixed architecture limits the types of project it can accommodate and thus was not appropriate for Folding@home.[82]

[edit] Graphics processing units

Folding@home also utilizes GPUs for distributed computing. Their specialized hardware is designed to accelerate rendering of 3D graphics applications such as video games and can significantly outperform CPUs for certain types of calculations. Although limited in generality, this makes GPUs one of the most powerful, cost-effective, and rapidly growing computational platforms. As such, general purpose GPU computing is the pursuit of many scientists and researchers. However, GPU hardware is difficult to utilize for non-graphics task and usually requires significant algorithm restructuring and an advanced understanding of the underlying architecture.[131] Such custom-tailoring is challenging, especially to researchers with limited software development resources. To achieve hardware-independence, the Pande lab's open source OpenMM library serves as an high-level API, allowing molecular simulation software to run efficiently on varying architectures without significant modification. Lower-level APIs interface the higher-level API with the underlying platform. This flexible approach delivers performance nearly equal to hand-tuned GPU code, and greatly outperforms CPU implementations.[132][133] GPUs remain Folding@home's most powerful platform in terms of FLOPS; as of February 2012, GPU clients account for 76% of the entire project's x86 FLOP throughput.[77]

Up until 2010, the computational reliability of GPGPU consumer-grade hardware had remained largely unknown, and circumstantial evidence related to the lack of built-in error detection and correction in GPU memory raised reliability concerns. The Pande lab then conducted the first large-scale test of GPU scientific accuracy on over 20,000 hosts on the Folding@home network. Small memory soft errors were detected in two-thirds of tested GPUs. The study found that the error rate was most dependent on board architecture, but concluded that reliable GPGPU computing was very feasible as long as attention is paid to the hardware characteristics.[134]

The first generation of Folding@home's Windows GPU client (GPU1) was released to the public on October 2, 2006,[130] delivering a 20-30X speedup for certain calculations over its CPU-based Gromacs counterparts.[135] It was the first time GPUs had been used for either distributed computing or major molecular dynamics calculations.[136][137] Pande lab gained significant knowledge and experience with the development of GPGPU software, but citing a need to improve scientific accuracies over DirectX,[135][138] it was succeeded by GPU2, the second generation successor of the client on April 10, 2008.[139] Following its introduction, GPU1 was officially retired on June 6.[140] Compared to GPU1, GPU2 was more scientifically reliable and productive, ran on ATI and CUDA-enabled Nvidia GPUs, and supported more advanced algorithms, larger proteins, and real-time visualization of the protein simulation.[141][142] Following this, the third generation of Folding@home's GPU client (GPU3) was released on May 25, 2010. While backwards compatible to GPU2, GPU3 is comparatively more stable and efficient, has additional scientific capabilities,[143][144] and uses OpenMM on top of an OpenCL framework.[144][145] Although it does not natively support the Linux operating system, it can be run under WINE for donors with Nvidia graphics cards.[146][147]

[edit] PlayStation 3

Further information: Life with PlayStation
The PlayStation 3's Life With PlayStation client displays a 3D animation of the protein being folded

Folding@home can also take advantage of the computing power of PlayStation 3s. At the time of its inception and for certain calculations, its main streaming Cell processor delivered a 20x speed increase over PCs, power which could not be found on other systems such as the Xbox 360.[84][29] The PS3's high speed and efficiency introduced other opportunities for worthwhile optimizations, and significantly changed the tradeoff between computational efficiency and overall accuracy, allowing for the utilization of more complex molecular models at little extra computational cost.[148] This allows the Pande lab to run biomedical calculations that would otherwise be computationally infeasible.[149]

The PS3 client was first released as a standalone application to the PlayStation 3 on March 23, 2008, developed in a collaborative effort between Sony and the Pande lab.[150] Its release made Folding@home the first distributed computing project to utilize PS3s.[151] Several months later the PS3 client became a channel of Life with PlayStation on its launch on September 18, 2008.[152][153] The client takes the middle ground between a CPU's flexibility and a GPU's speed, performing a limited set of calculations rapidly while still retaining adaptable.[130] However, unlike CPUs and GPUs, donors cannot perform other activities on their PS3 while running Folding@home.[149] The PS3's uniform console environment makes support easier, as well as making Folding@home user friendly.[29] The PS3 also has the ability to stream data quickly to its GPU, allowing for real-time atomic detail visualizations of the protein dynamics.[148]

[edit] Multi-core processing client

Folding@home can also utilize the parallel processing capabilities of modern multi-core processors to deliver over a 4x speedup over the standard uniprocessor clients.[120] These cores work together to complete a single WU significantly faster, which reduces the traditional difficulties of scaling a large simulation to many processors. While this approach is not only scientifically valuable, some of the Pande lab publications would not have been possible without this computing power.[154]

In November 2006, first generation symmetric multiprocessing (SMP) clients were publicly released for open beta testing, referred to as SMP1.[130] These clients used Message Passing Interface (MPI) communication protocols for parallel processing, as at the time the Gromacs cores were not designed to be used with multiple threads.[120] This was the first time a distributed computing project had utilized MPI, as it had previously been reserved only for supercomputers.[155] Although the clients performed well in Unix-based operating systems such as Linux and Mac's OS-X, they were particularly troublesome in Windows.[154][155] Despite these difficulties, SMP1 generated significant results that would have been impossible otherwise and which represented a landmark in the simulation of protein folding.[154] Following it, the second generation of the SMP client was released as an open beta on January 24, 2010, and as it replaced the complex MPI with a thread-based implementation, subsequently superseded SMP1.[156][157] The SMP2 client also supported a bonus points system, which non-linearly rewards additional points to donors for quick and reliable WU returns and for contributing to next-generation capabilities.[120] Donors who run the SMP2 client receive these extra points if they use a passkey and maintained an 80% successful return of Work Units.[156]

SMP2 also supports a special category of "bigadv" WUs, designed for simulating proteins that are unusually large and computationally intensive but have a great scientific priority. These WUs originally required minimum of eight CPU cores, but on February 7, 2012 this was increased to sixteen cores.[158][159] Compared to standard WUs run on SMP2, these also require more system resources such as RAM, but users who run these are rewarded with a 20% increase over SMP2's bonus point system.[160] The bigadv category allows Folding@home to run particularly demanding simulations on long timescales which that had previously required the use of supercomputing clusters and could not be performed anywhere else on Folding@home.[158]

[edit] V7

A sample image of the V7 client in Novice mode running under Windows 7. In addition to a variety of controls and user details, V7 also presents Work Unit information, such as its state, calculation progress, ETA, credit points, identification numbers, and description.

The V7 client is the seventh and latest generation of the Folding@home software, currently under development, but available for open beta testing.[4] It is a complete rewrite and unification of the previous clients for Microsoft Windows, Mac OS X and Linux operating systems.[161][162] Like its predecessors, V7 runs Folding@home in the background at very low priority, which allows other applications to use CPU resources as they need.[161] The V7 client is designed to make the installation, start-up, and operation user-friendly for novices, as well as offer greater scientific flexibility than previous clients.[161][163]

V7 consists of several elements. The user interacts with V7's GUI, known as FAHControl. It has Novice, Advanced, and Expert user interface modes, and has the ability to monitor, configure, and control many remote folding clients from a single computer.[161] FAHControl can monitor and direct FAHClient, which runs behind the scenes and in turn manages each FAHSlot (or "slot"). These slots act as replacements for the previously distinct FAH clients, as they may be of Uniprocessor, SMP, or GPU type. Each slot also contains a core and data associated with it, and can download, process, and upload Work Units independently. The FAHViewer function, modeled after the PS3 viewer, displays a real-time 3D rendering, if available, of the protein currently being processed.[161][164]

[edit] Work Units

The Work Unit (WU) is the protein data that the client is being asked to process. Work Units are a fraction of the simulation calculating the rate of transitions between the states in a Markov state model. The client connects to the Folding@home server to retrieve this Work Unit and may also download an appropriate core. Once completed, the results are returned and the respective credit points are awarded, and this cycle then repeats automatically.[82] During this exchange, all Work Units are validated using of 2048-bit digital signatures, and all WUs have associated deadlines. If this deadline is exceeded, the user may not get credit and the unit will be automatically reissued to another donor.[122] As protein folding is serial in nature and each WU is generated from its predecessor, this allows the overall simulation process to proceed normally if a WU is not returned after a certain period of time.[122] Due to these deadlines, the minimum system requirements for Folding@home is a Pentium 3 450 MHz CPU with SSE or newer.[122] However, Work Units for high performance clients have a much shorter deadline than those for the uniprocessor client, as a major part of the scientific benefit is dependent on rapidly completing simulations.[120]

Before public release, Work Units go through several quality assurance steps to keep problematic WUs from becoming fully available. These stages include internal testing, closed beta testing, and open beta testing, before a final full release across all of FAH.[165] Folding@home's Work Units are normally processed only once, except in the rare event that errors occur during processing of a WU. If this occurs for three different donors,[166] it is automatically pulled from distribution.[167][168] Topics in the Folding@home forum can be used to differentiate between problematic hardware and an actual bad Work Unit.[169]

[edit] Cores

Main article: Folding@home cores

Specialized scientific computer programs, referred to as "cores," perform the calculations on the Work Unit behind the scenes.[82] Folding@home's cores modified and optimized versions of molecular dynamics programs, including GROMACS, AMBER, TINKER, ProtoMol, CPMD, SHARPEN, and Desmond.[170][171][172] Some of these cores perform explicit atom-by-atom molecular dynamics calculations,[173] while others perform implicit solvation methods, which treat atoms as a mathematical continuum.[133][174] These cores are open-source software or are under similar licenses,[170][175] and are verified during download by 2048-bit digital signatures. While the same core can be used by various versions of the client, separating the core from the client enables the scientific methods to be updated automatically as needed without a client update.[82]

[edit] Comparison to other molecular systems

Rosetta@home is a distributed computing project aimed at protein structure prediction and is one of the most accurate tertiary structure predictors available.[176][177] As Rosetta only predicts the final folded state, and not how do proteins fold, Rosetta@home and Folding@home address very different molecular questions.[178][179] The Pande lab can use Rosetta's software to improve the efficiency of some Folding@home's projects.[179] Conversely, structure prediction algorithms can be improved from thermodynamic and kinetic models and the sampling aspects of protein folding simulations.[180][181] However, Folding@home and Rosetta@home perform complementary work.[182]

Anton is a special-purpose supercomputer constructed for molecular dynamics simulations. It is unique in its ability to produce individual ultra-long molecular trajectories on biological timescales. These simulations, while computationally expensive, contain more phase space than any one of Folding@home's many shorter trajectories. Like Folding@home, it has also improved several long-held theories of protein folding.[183] As of October 2011, Anton and FAH are the two most powerful molecular dynamics systems,[184] and Anton has also run individual simulations out to the millisecond range.[185][186] In 2011, the Pande lab built a Markov state model from a 200-μs Anton simulation. The publication demonstrated that an MSM built from serial data revealed folding information unobtainable with traditional approaches and that there was little difference between Markov models constructed from Anton's fewer long trajectories or one assembled from Folding@home's many shorter trajectories.[183] Starting in June 2011, Folding@home began additional sampling of an Anton simulation in an effort to better determine how its techniques compare to Anton's more traditional methods.[187][188] It is probable that a combination of Anton's and FAH's simulation methods would provide both a well-sampled simulation and completely cover the protein's phase space.[183]

[edit] See also

[edit] External links

[edit] Notes

Note 1: Supercomputer FLOP performance is assessed by running the legacy LINPACK benchmark. This short-term testing has difficulty in accurately reflect sustained performance on real-world tasks because LINPACK more efficiently maps to supercomputer hardware. Computing systems also vary in architecture and design, so direct comparison is difficult. Despite this, FLOPS remain the primary speed metric used in supercomputing. Wall clock time is a more accurate method of determining actual performance. Folding@home measures its FLOPS by timing how long each computer takes to complete its Work Unit (which is of known computational size) and then totals all systems across its network.[106][189][190][191]

[edit] References

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  69. ^ a b c 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 Physics 123 (8): 084108. Bibcode 2005JChPh.123h4108F. doi:10.1063/1.1999637. PMID 16164283. 
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  78. ^ "BOINC Combined Credit Overview". http://boincstats.com/stats/project_graph.php?pr=bo. Retrieved 2012-01-26. 
  79. ^ Michael R. Shirts and Vijay S. Pande (2001). "Mathematical Foundations of Coupled Parallel Simulations". Physical Review Letters 86 (22): 4983–4987. Bibcode 2001PhRvL..86.4983S. doi:10.1103/PhysRevLett.86.4983. PMID 11384401. 
  80. ^ Joshua Topolsky (2007-10-31). "Folding@Home recognized by Guinness World Records". http://www.engadget.com/2007/10/31/folding-home-recognized-by-guinness-world-records/. Retrieved 2007-11-05. 
  81. ^ Caroline Hadley (2004). "Biologists think bigger". EMBO reports 12 (5): 236–238. doi:10.1038/sj.embor.7400108. http://www.nature.com/embor/journal/v5/n3/full/7400108.html. 
  82. ^ a b c d e f Adam Beberg, Daniel Ensign, Guha Jayachandran, Siraj Khaliq, Vijay Pande (2009). "Folding@home: Lessons From Eight Years of Volunteer Distributed Computing". Parallel & Distributed Processing, IEEE International Symposium: 1–8. doi:10.1109/IPDPS.2009.5160922. ISBN 978-1-4244-3751-1. ISSN 1530-2075. http://www.hicomb.org/papers/HICOMB2009-13.pdf. 
  83. ^ Shankland, Stephen (March 22, 2002). "Google takes on supercomputing". CNet News. http://news.cnet.com/2100-1001-867091.html. 
  84. ^ a b c "Futures in Biotech 27: Folding@home at 1.3 Petaflops" (Interview, webcast). 2007-12-28. http://castroller.com/Podcasts/FuturesInBiotech/249153. 
  85. ^ ChelseaOilman (2005-12-30). "Google is after your CPU cycles". http://hardforum.com/showpost.php?p=1028770683&postcount=4. Retrieved 2011-09-06. 
  86. ^ Google (2007). "Your computer's idle time is too precious to waste". http://toolbar.google.com/dc/offerdc.html/. Retrieved 2011-09-06. 
  87. ^ "Genome@home Updates". 2002-03-04. http://www.stanford.edu/group/pandegroup/genome/new.html. Retrieved 2011-09-05. 
  88. ^ Pande lab. "Genome@home FAQ" (FAQ). Stanford University. http://genomeathome.stanford.edu/faq.html. Retrieved 2011-09-05. 
  89. ^ "TOP500 List - June 2007". Top500. 06-2007. http://www.top500.org/list/2007/06/100. Retrieved 2011-11-11. 
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  92. ^ "TOP500 List - November 2008". Top500. 11-2008. http://www.top500.org/list/2008/11/100. Retrieved 2011-11-11. 
  93. ^ "TOP500 List - June 2011". Top500. 06-2011. http://www.top500.org/list/2011/06/100. Retrieved 2011-11-11. 
  94. ^ Vijay Pande (2007-09-16). "Crossing the petaFLOPS barrier". http://folding.typepad.com/news/2007/09/crossing-the-pe.html. Retrieved 2011-08-28. 
  95. ^ David Nagel (2007-09-19). "Folding@home Achieves Petaflop Milestone - PS3 owners help scientists speed up their research". http://games.ign.com/articles/821/821350p1.html. Retrieved 2011-09-06. 
  96. ^ Mark Wilson (2007-03-25). "PS3 Folding@Home TFLOP Rating Demoted by 50%, PFLOPS Still Possible". http://gizmodo.com/246900/breaking-ps3-foldinghome-tflop-rating-demoted-by-50-pflops-still-possible. Retrieved 2011-09-14. 
  97. ^ Tim Hanlon (2007-03-09). "Playstation 3 continues to top Folding@Home statistics". http://www.gizmag.com/go/7086/. Retrieved 2011-09-14. 
  98. ^ "Folding@Home reach 2 Petaflops". 2008-05-08. http://n4g.com/news/143113/ps3-andamp-foldingahome-reach-2-petaflops/com. Retrieved 2011-09-23. 
  99. ^ "NVIDIA Achieves Monumental Folding@Home Milestone With Cuda". 2008-08-26. http://www.nvidia.com/object/io_1219747545128.html. Retrieved 2011-09-06. 
  100. ^ "3 PetaFLOP barrier". 2008-08-19. http://www.longecity.org/forum/topic/23841-3-petaflop-barrier/. Retrieved 2011-09-23. 
  101. ^ Vijay Pande (2009-02-18). "Folding@home Passes the 5 petaFLOP Mark". http://folding.typepad.com/news/2009/02/foldinghome-passes-the-5-petaflop-mark.html. Retrieved 2011-08-31. 
  102. ^ "Crossing the 5 petaFLOPS barrier". 2009-02-18. http://www.longecity.org/forum/topic/26449-crossing-the-5-petaflops-barrier/. Retrieved 2011-09-23. 
  103. ^ Jesse_V (2011-11-10). "Six Native PetaFLOPS". http://foldingforum.org/viewtopic.php?f=16&t=20011#p198840. Retrieved 2011-11-11. 
  104. ^ Vijay Pande (2009-03-18). "FLOPS". http://folding.typepad.com/news/2009/03/flops.html. Retrieved 2011-10-11. 
  105. ^ a b Pande lab (2009-04-04). "Folding@home FLOP FAQ" (FAQ). Stanford University. http://folding.stanford.edu/English/FAQ-flops. Retrieved 2011-08-28. 
  106. ^ a b Pande lab (2009-02-05). "PS3 FAQ" (FAQ). Stanford University. http://folding.stanford.edu/FAQ-PS3.html. Retrieved 2011-09-05. 
  107. ^ a b Pande lab (2011-02-16). "Folding@home Points FAQ" (FAQ). Stanford University. http://folding.stanford.edu/English/FAQ-Points. Retrieved 2011-08-31. 
  108. ^ a b Pande lab (2011-02-16). "Folding@home Points FAQ (New Benchmark Machine -- January 2010)" (FAQ). Stanford University. http://folding.stanford.edu/English/FAQ-PointsNew. Retrieved 2011-08-31. 
  109. ^ Peter Kasson (Pande lab member) (2010-01-24). "Upcoming Release of SMP2 Cores". http://foldingforum.org/viewtopic.php?f=24&t=13038. Retrieved 2011-08-31. 
  110. ^ Bruce Borden (bruce) (2010-04-07). "Re: Answers to: Reasons for not using F@H". http://foldingforum.org/viewtopic.php?f=16&t=1164&start=165#p138646. Retrieved 2011-09-05. 
  111. ^ Pande lab (2011-05-24). "Folding@home Passkey FAQ" (FAQ). Stanford University. http://folding.stanford.edu/English/FAQ-passkey. Retrieved 2011-09-06. 
  112. ^ "Default Team". http://fah-web.stanford.edu/cgi-bin/main.py?qtype=teampage&teamnum=0. Retrieved 2011-09-06. 
  113. ^ MtM (2009-12-17). "Re: New to F@H need startup info". http://foldingforum.org/viewtopic.php?f=47&t=12499#p122465. Retrieved 2011-09-30. 
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  115. ^ "Official Extreme Overclocking Folding@Home Team Forum". Extreme Overclocking. http://forums.extremeoverclocking.com/forumdisplay.php?f=45. Retrieved 2011-09-08. 
  116. ^ Norman Chan (2009-04-06). "Help Maximum PC's Folding Team Win the Next Chimp Challenge!". http://www.maximumpc.com/article/news/help_maximum_pcs_folding_team_win_next_chimp_challenge. Retrieved 2011-09-06. 
  117. ^ "Team 24 Folding at Home - March Challenge". 2011-02-20. http://forums.overclockers.com.au/showthread.php?t=940743. Retrieved 2011-09-06. 
  118. ^ "Announcing the F@H Prize". Immortality Institute. http://imminst.org/announcing-foldinghome-prize. Retrieved 2011-09-06. 
  119. ^ "Third Party Contributions - Stats Pages". 2011. http://fahwiki.net/index.php/Third_Party_Contributions#Stats_Pages. Retrieved 2011-08-07. 
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  121. ^ a b Pande lab (2011-02-10). "Windows Uniprocessor Client Installation Guide". Stanford University. http://folding.stanford.edu/English/WinUNIGuide. Retrieved 2011-09-05. 
  122. ^ a b c d e f g Pande lab (2011-08-18). "Folding@home Main FAQ" (FAQ). Stanford University. http://folding.stanford.edu/English/FAQ-main. Retrieved 2011-10-23. 
  123. ^ Bruce Borden (bruce) (2011-09-11). "Re: F@h Advertisement Techniques". http://foldingforum.org/viewtopic.php?f=16&t=19555#p195220. Retrieved 2011-10-18. 
  124. ^ Mohammad "PantherX" Syed (2010-09-02). "Re: Can Folding@home damage any part of my PC?". http://foldingforum.org/viewtopic.php?f=50&t=15863#p157125. Retrieved 2012-02-25. 
  125. ^ Bruce Borden (bruce) (2008-07-28). "Re: Answers to: Reasons for not using F@H". http://foldingforum.org/viewtopic.php?f=16&t=1164&start=75#p43364. Retrieved 2011-09-05. 
  126. ^ Vijay Pande (2009-08-20). "Importance of software and data integrity". http://folding.typepad.com/news/2009/08/importance-of-software-and-data-integrity.html. Retrieved 2011-10-19. 
  127. ^ a b c Bruce Borden (bruce) (2011-07-18). "Re: Advice for a new user". http://foldingforum.org/viewtopic.php?f=61&t=19163#p191513. Retrieved 2011-09-11. 
  128. ^ a b Pande lab (2009-11-19). "Uninstalling Folding@home FAQ". Stanford University. http://folding.stanford.edu/English/FAQ-Uninstall. Retrieved 2011-09-21. 
  129. ^ M. R. Shirts and V. S. Pande. (2000). "Screen Savers of the World, Unite!". Science 290 (5498): 1903–1904. doi:10.1126/science.290.5498.1903. PMID 17742054. 
  130. ^ a b c d Pande lab (2010-05-13). "High Performance FAQ" (FAQ). Stanford University. http://folding.stanford.edu/English/FAQ-highperformance. Retrieved 2011-09-05. 
  131. ^ John D. Owens, David Luebke, Naga Govindaraju, Mark Harris, Jens Krüger, Aaron Lefohn, Timothy J. Purcell (2007). "A Survey of General-Purpose Computation on Graphics Hardware". Computer Graphics Forum 26 (1): 80–113. doi:10.1111/j.1467-8659.2007.01012.x. 
  132. ^ P. Eastman and V. S. Pande (2010). "OpenMM: A Hardware Abstraction Layer for Molecular Simulations". Computing in Science & Engineering 12 (4): 34–39. doi:10.1109/MCSE.2010.27. 
  133. ^ a b 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 30 (6): 864–72. doi:10.1002/jcc.21209. PMC 2724265. PMID 19191337. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2724265. 
  134. ^ I. Haque and V. S. Pande (2010). "Hard Data on Soft Errors: A Large-Scale Assessment of Real-World Error Rates in GPGPU". 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid): 691–696. doi:10.1109/CCGRID.2010.84. ISBN 978-1-4244-6987-1. 
  135. ^ a b Pande lab (2011-03-18). "ATI FAQ" (FAQ). Stanford University. http://folding.stanford.edu/English/FAQ-ATI. Retrieved 2011-08-31. 
  136. ^ Vijay Pande (2008-05-23). "GPU news (about GPU1, GPU2, & NVIDIA support)". http://folding.typepad.com/news/2008/05/gpu-news-gpu1-g.html. Retrieved 2011-09-08. 
  137. ^ Travis Desell1, Anthony Waters, Malik Magdon-Ismail, Boleslaw K. Szymanski, Carlos A. Varela, Matthew Newby, Heidi Newberg, Andreas Przystawik, and David Anderson (2009). "Accelerating the MilkyWay@Home volunteer computing project with GPUs". 8th International Conference on Parallel Processing and Applied Mathematics (PPAM 2009). doi:10.1.1.158.7614. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.158.7614&rep=rep1&type=pdf. 
  138. ^ Vijay Pande (2008-05-27). "More info about the GPU1 to GPU2 transition". http://folding.typepad.com/news/2008/05/gpu1-to-gpu2-transition-why-does-gpu1-need-to-end.html. Retrieved 2011-09-07. 
  139. ^ Vijay Pande (2008-04-10). "GPU2 open beta". http://folding.typepad.com/news/2008/04/gpu2-open-beta.html. Retrieved 2011-09-07. 
  140. ^ Vijay Pande (2008-06-06). "GPU1 has been retired, GPU2 for NVIDIA release nearing". http://folding.typepad.com/news/2008/06/gpu1-has-been-retired-gpu2-for-nvidia-release-nearing.html. Retrieved 2011-09-07. 
  141. ^ Vijay Pande (2008-04-15). "Updates to the Download page/GPU2 goes live". http://folding.typepad.com/news/2008/04/updates-to-the.html. Retrieved 2011-09-07. 
  142. ^ Vijay Pande (2008-04-11). "GPU2 open beta going well". http://folding.typepad.com/news/2008/04/gpu2-open-bet-1.html. Retrieved 2011-09-07. 
  143. ^ Vijay Pande (2008-01-25). "Code Development Updates". http://folding.typepad.com/news/2008/01/code-developmen.html. Retrieved 2011-09-07. 
  144. ^ a b Vijay Pande (2010-04-24). "Prepping for the GPU3 rolling: new client and NVIDIA FAH GPU clients will (in the future) need CUDA 2.2 or later". http://folding.typepad.com/news/2010/04/prepping-for-the-gpu3-rolling-new-client-and-nvidia-fah-gpu-clients-will-need-cuda-22-or-later.html. Retrieved 2011-09-08. 
  145. ^ Vijay Pande (2010-05-25). "Folding@home: Open beta release of the GPU3 client/core". http://folding.typepad.com/news/2010/05/open-beta-release-of-the-gpu3-clientcore.html. Retrieved 2011-09-07. 
  146. ^ Joseph Coffland (Pande lab member) (2011-10-13). "Re: FAHClient V7.1.38 released (4th Open-Beta)". http://foldingforum.org/viewtopic.php?f=67&t=19795&start=45#p197198. Retrieved 2011-10-15. 
  147. ^ "NVIDIA GPU3 Linux/Wine Headless Install Guide". 2008-11-08. http://foldingforum.org/viewtopic.php?f=54&t=6793. Retrieved 2011-09-05. 
  148. ^ a b 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 Chemistry 30 (2): 268–274. doi:10.1002/jcc.21054. PMID 18615421. 
  149. ^ a b David E. Williams (2006-10-20). "PlayStation's serious side: Fighting disease". CNN. http://edition.cnn.com/2006/TECH/fun.games/09/18/playstation.folding/. Retrieved 2011-10-16. 
  150. ^ Roger Altizer (2008). "Folding@home to Go Live on the PlayStation 3 on March 23rd". About.com - PlayStation Games. http://playstation.about.com/od/ps3/a/PSFoldingAtHome.htm. Retrieved 2012-02-24. 
  151. ^ Lou Kesten, Associated Press (2007-03-26). "Week in video-game news: 'God of War II' storms the PS2; a PS3 research project". Pittsburgh Post-Gazette. http://www.post-gazette.com/pg/07085/772011-96.stm. Retrieved 2011-12-13. 
  152. ^ Elaine Chow (2008-09-18). "PS3 News Service, Life With Playstation, Now Up For Download". Gizmodo. http://gizmodo.com/5051558/ps3-news-service-life-with-playstation-now-up-for-download. Retrieved 2012-02-24. 
  153. ^ Vijay Pande (2008-09-18). "Life with Playstation -- a new update to the FAH/PS3 client". http://folding.typepad.com/news/2008/09/life-with-playstation.html. Retrieved 2012-02-24. 
  154. ^ a b c Vijay Pande (2008-06-15). "What does the SMP core do?". http://folding.typepad.com/news/2008/06/what-does-the-smp-core-do.html. Retrieved 2011-09-07. 
  155. ^ a b Vijay Pande (2008-03-08). "New Windows client/core development (SMP and classic clients)". http://folding.typepad.com/news/2008/03/new-windows-cli.html. Retrieved 2011-09-30. 
  156. ^ a b Peter Kasson (Pande lab member) (2010-01-24). "upcoming release of SMP2 cores". http://foldingforum.org/viewtopic.php?f=24&t=13038#p127406. Retrieved 2011-09-30. 
  157. ^ Vijay Pande (2009-06-17). "How does FAH code development and sysadmin get done?". http://folding.typepad.com/news/2009/06/how-does-fah-code-development-and-sysadmin-get-done.html. Retrieved 2011-10-14. 
  158. ^ a b Peter Kasson (Pande lab member) (2009-07-15). "new release: extra-large work units". http://foldingforum.org/viewtopic.php?t=10697. Retrieved 2011-10-09. 
  159. ^ Vijay Pande (2011-02-07). "Update on "bigadv-16", the new bigadv rollout". http://folding.typepad.com/news/2012/02/update-on-bigadv-16-the-new-bigadv-rollout.html. Retrieved 2012-02-09. 
  160. ^ Vijay Pande (2011-07-02). "Change in the points system for bigadv work units". http://folding.typepad.com/news/2011/07/change-in-the-points-system-for-bigadv-work-units.html. Retrieved 2012-02-24. 
  161. ^ a b c d e Vijay Pande (2011-03-29). "Client version 7 now in open beta". http://folding.typepad.com/news/2011/03/client-version-7-now-in-open-beta.html. Retrieved 2011-08-14. 
  162. ^ Pande lab (2011). "Client FAQ" (FAQ). Stanford University. https://fah-web.stanford.edu/projects/FAHClient/wiki/ClientFAQ. Retrieved 2011-08-14. 
  163. ^ Vijay Pande (2011-03-31). "Core 16 for ATI released; also note on NVIDIA GPU support for older boards". http://folding.typepad.com/news/2011/03/core-16-for-ati-released-also-note-on-nvidia-gpu-support-for-older-boards.html. Retrieved 2011-09-07. 
  164. ^ Pande lab (2011-09-23). "Windows (FAH V7) Install Guide". Stanford University. http://folding.stanford.edu/English/WinGuide. Retrieved 2011-10-09. 
  165. ^ Vijay Pande (2011-04-05). "More transparency in testing". http://folding.typepad.com/news/2011/04/more-transparency-in-testing.html. Retrieved 2011-10-14. 
  166. ^ Bruce Borden (bruce) (2011-08-07). "Re: Gromacs Cannot Continue Further". http://foldingforum.org/viewtopic.php?f=59&t=19315#p192836. Retrieved 2011-08-07. 
  167. ^ Bruce Borden (bruce) (2011-08-09). "Re: Project: 6053 (Run 1, Clone 194, Gen 357)". http://foldingforum.org/viewtopic.php?f=19&t=19325&p=192927&hilit=8am#p192927. Retrieved 2011-08-09. 
  168. ^ Mohammad "PantherX" Syed (2011-10-01). "Re: Project 6803: (Run 4, Clone 66, Gen 255)". http://foldingforum.org/viewtopic.php?f=19&t=19725#p196444. Retrieved 2011-10-09. 
  169. ^ Mohammad "PantherX" Syed (2010-10-31). "Troubleshooting Bad WUs". http://foldingforum.org/viewtopic.php?f=19&t=16526. Retrieved 2011-08-07. 
  170. ^ a b Pande lab (2010-02-03). "Folding@home Open Source FAQ" (FAQ). Stanford University. http://folding.stanford.edu/English/FAQ-OpenSource. Retrieved 2011-09-11. 
  171. ^ Pande lab (2011-05-16). "Folding@home Frequently Asked Questions (FAQ) Index" (FAQ). Stanford University. http://folding.stanford.edu/English/FAQ. Retrieved 2012-02-24. 
  172. ^ Vijay Pande (2009-09-25). "Update on new FAH cores and clients". http://folding.typepad.com/news/2009/09/update-on-new-fah-cores-and-clients.html. Retrieved 2012-02-24. 
  173. ^ Vijay Pande (2007-09-26). "How FAH works: Molecular dynamics". http://folding.typepad.com/news/2007/09/how-fah-works-1.html. Retrieved 2011-10-14. 
  174. ^ Roux B, Simonson T (April 1999). "Implicit solvent models". Biophys. Chem. 78 (1–2): 1–20. doi:10.1016/S0301-4622(98)00226-9. ISSN 0301-4622. PMID 17030302. 
  175. ^ Vijay Pande (2011-09-06). "Re: Utilizing this resource". http://foldingforum.org/viewtopic.php?f=17&t=19545&p=194838#p194838. Retrieved 2011-09-11. 
  176. ^ Lensink MF, Méndez R, Wodak SJ (December 2007). "Docking and scoring protein complexes: CAPRI 3rd Edition". Proteins 69 (4): 704–18. doi:10.1002/prot.21804. PMID 17918726. 
  177. ^ Gregory R. Bowman and Vijay S. Pande (2009). "Simulated tempering yields insight into the low-resolution Rosetta scoring function". Proteins: Structure, Function, and Bioinformatics 74 (3): 777–88. doi:10.1002/prot.22210. PMID 18767152. 
  178. ^ Vijay Pande (2006-03-05). "Rosetta and Folding". http://web.archive.org/web/20070927193033/http://forum.folding-community.org/viewtopic.php?p=125338. Retrieved 2012-02-26. 
  179. ^ a b TJ Lane (Pande lab member) (2011-06-09). "Re: Course grained Protein folding in under 10 minutes". http://foldingforum.org/viewtopic.php?p=188496#p188496. Retrieved 2012-02-26. 
  180. ^ G. R. Bowman and V. S. Pande (2009). Hofmann, Andreas. ed. "The Roles of Entropy and Kinetics in Structure Prediction". PLoS ONE 4 (6): e5840. Bibcode 2009PLoSO...4.5840B. doi:10.1371/journal.pone.0005840. PMC 2688754. PMID 19513117. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2688754. 
  181. ^ 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 Biology 323 (1): 153–164. doi:10.1016/S0022-2836(02)00888-4. PMID 12368107. 
  182. ^ Vijay Pande (2008-04-26). "Re: collaborating with competition". http://foldingforum.org/viewtopic.php?f=15&t=2322&p=20638#p20642. Retrieved 2012-02-26. 
  183. ^ a b c Thomas J. Lane, Gregory R. Bowman, Kyle A Beauchamp, Vincent Alvin Voelz, and Vijay S. Pande (2011). "Markov State Model Reveals Folding and Functional Dynamics in Ultra-Long MD Trajectories". Journal of the American Chemical Society 133 (45): 18413–9. doi:10.1021/ja207470h. PMC 3227799. PMID 21988563. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3227799. 
  184. ^ Vijay Pande (2011-10-13). "Comparison between FAH and Anton's approaches". http://folding.typepad.com/news/2011/10/comparison-between-fah-and-antons-approaches.html. Retrieved 2012-02-25. 
  185. ^ David E. Shaw et al (2009). "Millisecond-scale molecular dynamics simulations on Anton". Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis (39): 1–11. doi:10.1145/1654059.1654099. ISBN 9781605587448. 
  186. ^ David E. Shaw et al (2010). "Atomic-Level Characterization of the Structural Dynamics of Proteins". Science 330 (6002): 341–346. doi:10.1126/science.1187409. PMID 20947758. 
  187. ^ TJ Lane (Pande lab member) (2011-06-06). "Project 7610 & 7611 in Beta". http://foldingforum.org/viewtopic.php?f=66&t=18822. Retrieved 2012-02-25. 
  188. ^ Pande lab (2011). "Project 7610 Description". http://fah-web.stanford.edu/cgi-bin/fahproject.overusingIPswillbebanned?p=7610. Retrieved 2012-02-26. 
  189. ^ Vijay Pande (2008-11-09). "Re: ATI and NVIDIA stats vs. PPD numbers". http://foldingforum.org/viewtopic.php?p=67416#p67416. Retrieved 2012-02-25. 
  190. ^ Imran "ihaque" Haque (Pande lab member) (2011-07-13). "Re: Are my conversion for GPU flops relativly correct?". http://foldingforum.org/viewtopic.php?f=16&t=19118#p191157. Retrieved 2012-02-25. 
  191. ^ Christopher Mims (2010-11-08). "Why China's New Supercomputer Is Only Technically the World's Fastest". MIT. http://www.technologyreview.com/blog/mimssbits/25981/. Retrieved 2012-02-25. 
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