Distributed computing is a field of computer science that studies distributed systems , defined as computer systems whose inter-communicating components are located on different networked computers .
148-493: Folding@home ( FAH or F@h ) is a distributed computing project aimed to help scientists develop new therapeutics for a variety of diseases by the means of simulating protein dynamics . This includes the process of protein folding and the movements of proteins , and is reliant on simulations run on volunteers' personal computers . Folding@home is currently based at the University of Pennsylvania and led by Greg Bowman ,
296-433: A closed-source license to help ensure data validity. Less active cores include ProtoMol and SHARPEN. Folding@home has used AMBER , CPMD , Desmond , and TINKER , but these have since been retired and are no longer in active service. Some of these cores perform explicit solvation calculations in which the surrounding solvent (usually water) is modeled atom-by-atom; while others perform implicit solvation methods, where
444-404: A literature review article. In 2008, Folding@home simulated the dynamics of Aβ aggregation in atomic detail over timescales of the order of tens of seconds. Prior studies were only able to simulate about 10 microseconds. Folding@home was able to simulate Aβ folding for six orders of magnitude longer than formerly possible. Researchers used the results of this study to identify a beta hairpin that
592-443: A solution for each instance. Instances are questions that we can ask, and solutions are desired answers to these questions. Theoretical computer science seeks to understand which computational problems can be solved by using a computer ( computability theory ) and how efficiently ( computational complexity theory ). Traditionally, it is said that a problem can be solved by using a computer if we can design an algorithm that produces
740-513: A Folding@home server and retrieves a work unit and may also download the appropriate core for the client's settings, operating system, and the underlying hardware architecture. After processing, the work unit is returned to the Folding@home servers. Computer clients are tailored to uniprocessor and multi-core processor systems, and graphics processing units . The diversity and power of each hardware architecture provides Folding@home with
888-405: A Markov state model is inversely proportional to the number of parallel simulations run, i.e., the number of processors available. In other words, it achieves linear parallelization , leading to an approximately four orders of magnitude reduction in overall serial calculation time. A completed MSM may contain tens of thousands of sample states from the protein's phase space (all the conformations
1036-488: A biomolecule's conformational and energy landscape as a set of distinct structures and the short transitions between them. The adaptive sampling Markov state model method significantly increases the efficiency of simulation as it avoids computation inside the local energy minimum itself, and is amenable to distributed computing (including on GPUGRID ) as it allows for the statistical aggregation of short, independent simulation trajectories. The amount of time it takes to construct
1184-474: A common goal for their work. The terms " concurrent computing ", " parallel computing ", and "distributed computing" have much overlap, and no clear distinction exists between them. The same system may be characterized both as "parallel" and "distributed"; the processors in a typical distributed system run concurrently in parallel. Parallel computing may be seen as a particularly tightly coupled form of distributed computing, and distributed computing may be seen as
1332-520: A correct solution for any given instance. Such an algorithm can be implemented as a computer program that runs on a general-purpose computer: the program reads a problem instance from input , performs some computation, and produces the solution as output . Formalisms such as random-access machines or universal Turing machines can be used as abstract models of a sequential general-purpose computer executing such an algorithm. The field of concurrent and distributed computing studies similar questions in
1480-428: A cure and learning more about the coronavirus pandemic . The initial wave of projects simulate potentially druggable protein targets from SARS-CoV-2 virus, and the related SARS-CoV virus, about which there is significantly more data available. Drugs function by binding to specific locations on target molecules and causing some desired change, such as disabling a target or causing a conformational change . Ideally,
1628-423: A deadlock. This problem is PSPACE-complete , i.e., it is decidable, but not likely that there is an efficient (centralised, parallel or distributed) algorithm that solves the problem in the case of large networks. Configuration space (physics) In classical mechanics , the parameters that define the configuration of a system are called generalized coordinates , and the space defined by these coordinates
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#17327733487551776-504: A decision problem can be solved in polylogarithmic time by using a polynomial number of processors, then the problem is said to be in the class NC . The class NC can be defined equally well by using the PRAM formalism or Boolean circuits—PRAM machines can simulate Boolean circuits efficiently and vice versa. In the analysis of distributed algorithms, more attention is usually paid on communication operations than computational steps. Perhaps
1924-456: A difference of several orders of magnitude. The development of models to predict the mechanisms of membrane fusion will assist in the scientific understanding of how to target the process with antiviral drugs. In 2006, scientists applied Markov state models and the Folding@home network to discover two pathways for fusion and gain other mechanistic insights. Following detailed simulations from Folding@home of small cells known as vesicles , in 2007,
2072-581: A drug should act very specifically, and bind only to its target without interfering with other biological functions. However, it is difficult to precisely determine where and how tightly two molecules will bind. Due to limits in computing power, current in silico methods usually must trade speed for accuracy ; e.g., use rapid protein docking methods instead of computationally costly free energy calculations . Folding@home's computing performance allows researchers to use both methods, and evaluate their efficiency and reliability. Computer-assisted drug design has
2220-494: A final full release across Folding@home. Folding@home's work units are normally processed only once, except in the rare event that errors occur during processing. If this occurs for three different users, the unit is automatically pulled from distribution. The Folding@home support forum can be used to differentiate between issues arising from problematic hardware and bad work units. Specialized molecular dynamics programs, referred to as "FahCores" and often abbreviated "cores", perform
2368-471: A former student of Vijay Pande . The project utilizes graphics processing units (GPUs), central processing units (CPUs), and ARM processors like those on the Raspberry Pi for distributed computing and scientific research. The project uses statistical simulation methodology that is a paradigm shift from traditional computing methods. As part of the client–server model network architecture ,
2516-573: A greater scientific priority. Users may also receive credit for their work by clients on multiple machines. This point system attempts to align awarded credit with the value of the scientific results. Users can register their contributions under a team, which combine the points of all their members. A user can start their own team, or they can join an existing team. In some cases, a team may have their own community-driven sources of help or recruitment such as an Internet forum . The points can foster friendly competition between individuals and teams to compute
2664-499: A large and complex biochemical machine that performs protein biosynthesis by translating messenger RNA into proteins. Macrolide antibiotics clog the ribosome's exit tunnel, preventing synthesis of essential bacterial proteins. In 2007, the Pande lab received a grant to study and design new antibiotics. In 2008, they used Folding@home to study the interior of this tunnel and how specific molecules may affect it. The full structure of
2812-467: A local thermodynamic free energy minimum in the protein's energy landscape . Through a process known as adaptive sampling , these conformations are used by Folding@home as starting points for a set of simulation trajectories. As the simulations discover more conformations, the trajectories are restarted from them, and a Markov state model (MSM) is gradually created from this cyclic process. MSMs are discrete-time master equation models which describe
2960-401: A loosely coupled form of parallel computing. Nevertheless, it is possible to roughly classify concurrent systems as "parallel" or "distributed" using the following criteria: The figure on the right illustrates the difference between distributed and parallel systems. Figure (a) is a schematic view of a typical distributed system; the system is represented as a network topology in which each node
3108-482: A mechanical system forms the cotangent bundle T ∗ Q {\displaystyle T^{*}Q} of the configuration manifold Q {\displaystyle Q} . This larger manifold is called the phase space of the system. In quantum mechanics , configuration space can be used (see for example the Mott problem ), but the classical mechanics extension to phase space cannot. Instead,
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#17327733487553256-431: A much wider sense, even referring to autonomous processes that run on the same physical computer and interact with each other by message passing. While there is no single definition of a distributed system, the following defining properties are commonly used as: A distributed system may have a common goal, such as solving a large computational problem; the user then perceives the collection of autonomous processors as
3404-501: A pathological marker of Alzheimer's disease. Due to the heterogeneous nature of these aggregates, experimental methods such as X-ray crystallography and nuclear magnetic resonance (NMR) have had difficulty characterizing their structures. Moreover, atomic simulations of Aβ aggregation are highly demanding computationally due to their size and complexity. Preventing Aβ aggregation is a promising method to developing therapeutic drugs for Alzheimer's disease, according to Naeem and Fazili in
3552-424: A problem is divided into many tasks, each of which is solved by one or more computers, which communicate with each other via message passing. The word distributed in terms such as "distributed system", "distributed programming", and " distributed algorithm " originally referred to computer networks where individual computers were physically distributed within some geographical area. The terms are nowadays used in
3700-404: A protein can take on) and the transitions between them. The model illustrates folding events and pathways (i.e., routes) and researchers can later use kinetic clustering to view a coarse-grained representation of the otherwise highly detailed model. They can use these MSMs to reveal how proteins misfold and to quantitatively compare simulations with experiments. Between 2000 and 2010, the length of
3848-452: A protein that can break down antibiotics like penicillin . Chemical activity occurs along a protein's active site . Traditional drug design methods involve tightly binding to this site and blocking its activity, under the assumption that the target protein exists in one rigid structure. However, this approach works for approximately only 15% of all proteins. Proteins contain allosteric sites which, when bound to by small molecules, can alter
3996-423: A protein's conformation and ultimately affect the protein's activity. These sites are attractive drug targets, but locating them is very computationally costly . In 2012, Folding@home and MSMs were used to identify allosteric sites in three medically relevant proteins: beta-lactamase, interleukin-2 , and RNase H . Approximately half of all known antibiotics interfere with the workings of a bacteria's ribosome ,
4144-452: A rather different set of formalisms and notation are used in the analogous concept called quantum state space . The analog of a "point particle" becomes a single point in C P 1 {\displaystyle \mathbb {C} \mathbf {P} ^{1}} , the complex projective line , also known as the Bloch sphere . It is complex, because a quantum-mechanical wave function has
4292-588: A reasonable period of time. Due to these deadlines, the minimum system requirement for Folding@home is a Pentium 3 450 MHz CPU with Streaming SIMD Extensions (SSE). 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. Before public release, work units go through several quality assurance steps to keep problematic ones from becoming fully available. These testing stages include internal, beta, and advanced, before
4440-455: A robot arm to obtain a particular end-effector location, and it is even possible to have the robot arm move while keeping the end effector stationary. Thus, a complete description of the arm, suitable for use in kinematics, requires the specification of all of the joint positions and angles, and not just some of them. The joint parameters of the robot are used as generalized coordinates to define configurations. The set of joint parameter values
4588-654: A schematic architecture allowing for live environment relay. This enables distributed computing functions both within and beyond the parameters of a networked database. Reasons for using distributed systems and distributed computing may include: Examples of distributed systems and applications of distributed computing include the following: According to Reactive Manifesto, reactive distributed systems are responsive, resilient, elastic and message-driven. Subsequently, Reactive systems are more flexible, loosely-coupled and scalable. To make your systems reactive, you are advised to implement Reactive Principles. Reactive Principles are
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4736-405: A sequential general-purpose computer? The discussion below focuses on the case of multiple computers, although many of the issues are the same for concurrent processes running on a single computer. Three viewpoints are commonly used: In the case of distributed algorithms, computational problems are typically related to graphs. Often the graph that describes the structure of the computer network
4884-457: A set of principles and patterns which help to make your cloud native application as well as edge native applications more reactive. Many tasks that we would like to automate by using a computer are of question–answer type: we would like to ask a question and the computer should produce an answer. In theoretical computer science , such tasks are called computational problems . Formally, a computational problem consists of instances together with
5032-434: A shared language and online culture. This pattern of participation has been observed in other distributed computing projects. Another key observation of Folding@home participants is that many are male. This has also been observed in other distributed projects. Furthermore, many participants work in computer and technology-based jobs and careers. Not all Folding@home participants are hardware enthusiasts. Many participants run
5180-402: A subspace of the n {\displaystyle n} -rigid-body configuration space. Note, however, that in robotics, the term configuration space can also refer to a further-reduced subset: the set of reachable positions by a robot's end-effector . This definition, however, leads to complexities described by the holonomy : that is, there may be several different ways of arranging
5328-471: A system refers to the position of all constituent point particles of the system. The configuration space is insufficient to completely describe a mechanical system: it fails to take into account velocities. The set of velocities available to a system defines a plane tangent to the configuration manifold of the system. At a point q ∈ Q {\displaystyle q\in Q} , that tangent plane
5476-695: A token ring network in which the token has been lost. Coordinator election algorithms are designed to be economical in terms of total bytes transmitted, and time. The algorithm suggested by Gallager, Humblet, and Spira for general undirected graphs has had a strong impact on the design of distributed algorithms in general, and won the Dijkstra Prize for an influential paper in distributed computing. Many other algorithms were suggested for different kinds of network graphs , such as undirected rings, unidirectional rings, complete graphs, grids, directed Euler graphs, and others. A general method that decouples
5624-411: A type of skeleton for cells , and as antibodies , while other proteins participate in the immune system . Before a protein can take on these roles, it must fold into a functional three-dimensional structure , a process that often occurs spontaneously and is dependent on interactions within its amino acid sequence and interactions of the amino acids with their surroundings. Protein folding is driven by
5772-434: A unit. Alternatively, each computer may have its own user with individual needs, and the purpose of the distributed system is to coordinate the use of shared resources or provide communication services to the users. Other typical properties of distributed systems include the following: Here are common architectural patterns used for distributed computing: Distributed systems are groups of networked computers which share
5920-488: A variety of structural roles and is the most abundant protein in mammals . The mutation causes a deformation in collagen's triple helix structure , which if not naturally destroyed, leads to abnormal and weakened bone tissue. In 2005, Folding@home tested a new quantum mechanical method that improved upon prior simulation methods, and which may be useful for future computing studies of collagen. Although researchers have used Folding@home to study collagen folding and misfolding,
6068-407: A wide range of biological functions. This fusion involves conformational changes of viral fusion proteins and protein docking , but the exact molecular mechanisms behind fusion remain largely unknown. Fusion events may consist of over a half million atoms interacting for hundreds of microseconds. This complexity limits typical computer simulations to about ten thousand atoms over tens of nanoseconds:
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6216-530: Is Q = R 3 {\displaystyle Q=\mathbb {R} ^{3}} . It is conventional to use the symbol q {\displaystyle q} for a point in configuration space; this is the convention in both the Hamiltonian formulation of classical mechanics , and in Lagrangian mechanics . The symbol p {\displaystyle p} is used to denote momenta;
6364-477: Is the problem instance. This is illustrated in the following example. Consider the computational problem of finding a coloring of a given graph G . Different fields might take the following approaches: While the field of parallel algorithms has a different focus than the field of distributed algorithms, there is much interaction between the two fields. For example, the Cole–Vishkin algorithm for graph coloring
6512-416: Is a computer and each line connecting the nodes is a communication link. Figure (b) shows the same distributed system in more detail: each computer has its own local memory, and information can be exchanged only by passing messages from one node to another by using the available communication links. Figure (c) shows a parallel system in which each processor has a direct access to a shared memory. The situation
6660-464: Is advantageous in studying aspects of folding, misfolding, and their relationships to disease that are difficult to observe experimentally. For example, in 2011, Folding@home simulated protein folding inside a ribosomal exit tunnel, to help scientists better understand how natural confinement and crowding might influence the folding process. Furthermore, scientists typically employ chemical denaturants to unfold proteins from their stable native state. It
6808-403: Is also focused on understanding the asynchronous nature of distributed systems: Note that in distributed systems, latency should be measured through "99th percentile" because "median" and "average" can be misleading. Coordinator election (or leader election ) is the process of designating a single process as the organizer of some task distributed among several computers (nodes). Before
6956-477: Is also used to study protein chaperones , heat shock proteins which play essential roles in cell survival by assisting with the folding of other proteins in the crowded and chemically stressful environment within a cell. Rapidly growing cancer cells rely on specific chaperones, and some chaperones play key roles in chemotherapy resistance. Inhibitions to these specific chaperones are seen as potential modes of action for efficient chemotherapy drugs or for reducing
7104-564: Is assessed by running the legacy LINPACK benchmark. This short-term testing has difficulty in accurately reflecting sustained performance on real-world tasks because LINPACK more efficiently maps to supercomputer hardware. Computing systems vary in architecture and design, so direct comparison is difficult. Despite this, FLOPS remain the primary speed metric used in supercomputing. In contrast, Folding@home determines its FLOPS using wall-clock time by measuring how much time its work units take to complete. On September 16, 2007, due in large part to
7252-501: Is associated with protein misfolding and aggregation. Excessive repeats of the glutamine amino acid at the N-terminus of the huntingtin protein cause aggregation, and although the behavior of the repeats is not completely understood, it does lead to the cognitive decline associated with the disease. As with other aggregates, there is difficulty in experimentally determining its structure. Scientists are using Folding@home to study
7400-419: Is available in their local D-neighbourhood . Many distributed algorithms are known with the running time much smaller than D rounds, and understanding which problems can be solved by such algorithms is one of the central research questions of the field. Typically an algorithm which solves a problem in polylogarithmic time in the network size is considered efficient in this model. Another commonly used measure
7548-403: Is better understood, therapies can be developed that augment cells' natural ability to regulate protein folding. Such therapies include the use of engineered molecules to alter the production of a given protein, help destroy a misfolded protein, or assist in the folding process. The combination of computational molecular modeling and experimental analysis has the possibility to fundamentally shape
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#17327733487557696-441: Is called the configuration space of the physical system . It is often the case that these parameters satisfy mathematical constraints, such that the set of actual configurations of the system is a manifold in the space of generalized coordinates. This manifold is called the configuration manifold of the system. Notice that this is a notion of "unrestricted" configuration space, i.e. in which different point particles may occupy
7844-403: Is called the joint space . A robot's forward and inverse kinematics equations define maps between configurations and end-effector positions, or between joint space and configuration space. Robot motion planning uses this mapping to find a path in joint space that provides an achievable route in the configuration space of the end-effector. In classical mechanics , the configuration of
7992-418: Is denoted by T q Q {\displaystyle T_{q}Q} . Momentum vectors are linear functionals of the tangent plane, known as cotangent vectors; for a point q ∈ Q {\displaystyle q\in Q} , that cotangent plane is denoted by T q ∗ Q {\displaystyle T_{q}^{*}Q} . The set of positions and momenta of
8140-490: Is described using generalized coordinates ; thus, three of the coordinates might describe the location of the center of mass of the rigid body, while three more might be the Euler angles describing its orientation. There is no canonical choice of coordinates; one could also choose some tip or endpoint of the rigid body, instead of its center of mass; one might choose to use quaternions instead of Euler angles, and so on. However,
8288-581: Is further complicated by the traditional uses of the terms parallel and distributed algorithm that do not quite match the above definitions of parallel and distributed systems (see below for more detailed discussion). Nevertheless, as a rule of thumb, high-performance parallel computation in a shared-memory multiprocessor uses parallel algorithms while the coordination of a large-scale distributed system uses distributed algorithms. The use of concurrent processes which communicate through message-passing has its roots in operating system architectures studied in
8436-452: Is interested in the case where the particles interact: for example, they are specific locations in some assembly of gears, pulleys, rolling balls, etc. often constrained to move without slipping. In this case, the configuration space is not all of R 3 n {\displaystyle \mathbb {R} ^{3n}} , but the subspace (submanifold) of allowable positions that the points can take. The set of coordinates that define
8584-478: Is necessary to interconnect processes running on those CPUs with some sort of communication system . Whether these CPUs share resources or not determines a first distinction between three types of architecture: Distributed programming typically falls into one of several basic architectures: client–server , three-tier , n -tier , or peer-to-peer ; or categories: loose coupling , or tight coupling . Another basic aspect of distributed computing architecture
8732-581: Is no different in that respect. Research carried out recently on over 400 active participants revealed that they wanted to help make a contribution to research and that many had friends or relatives affected by the diseases that the Folding@home scientists investigate. Folding@home attracts participants who are computer hardware enthusiasts. These groups bring considerable expertise to the project and are able to build computers with advanced processing power. Other distributed computing projects attract these types of participants and projects are often used to benchmark
8880-405: Is not generally known how the denaturant affects the protein's refolding, and it is difficult to experimentally determine if these denatured states contain residual structures which may influence folding behavior. In 2010, Folding@home used GPUs to simulate the unfolded states of Protein L , and predicted its collapse rate in strong agreement with experimental results. The large data sets from
9028-460: Is said to have six degrees of freedom . In this case, the configuration space Q = R 3 × S O ( 3 ) {\displaystyle Q=\mathbb {R} ^{3}\times \mathrm {SO} (3)} is six-dimensional, and a point q ∈ Q {\displaystyle q\in Q} is just a point in that space. The "location" of q {\displaystyle q} in that configuration space
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#17327733487559176-492: Is the method of communicating and coordinating work among concurrent processes. Through various message passing protocols, processes may communicate directly with one another, typically in a main/sub relationship. Alternatively, a "database-centric" architecture can enable distributed computing to be done without any form of direct inter-process communication , by utilizing a shared database . Database-centric architecture in particular provides relational processing analytics in
9324-410: Is the number of synchronous communication rounds required to complete the task. This complexity measure is closely related to the diameter of the network. Let D be the diameter of the network. On the one hand, any computable problem can be solved trivially in a synchronous distributed system in approximately 2 D communication rounds: simply gather all information in one location ( D rounds), solve
9472-511: Is the total number of bits transmitted in the network (cf. communication complexity ). The features of this concept are typically captured with the CONGEST(B) model, which is similarly defined as the LOCAL model, but where single messages can only contain B bits. Traditional computational problems take the perspective that the user asks a question, a computer (or a distributed system) processes
9620-540: The National Institutes of Health are testing it against a large variety of tumor models to try to accelerate its development as a therapeutic. Osteogenesis imperfecta , known as brittle bone disease, is an incurable genetic bone disorder which can be lethal. Those with the disease are unable to make functional connective bone tissue. This is most commonly due to a mutation in Type-I collagen , which fulfills
9768-584: The lack of a global clock , and managing the independent failure of components. When a component of one system fails, the entire system does not fail. Examples of distributed systems vary from SOA-based systems to microservices to massively multiplayer online games to peer-to-peer applications . Distributed systems cost significantly more than monolithic architectures, primarily due to increased needs for additional hardware, servers, gateways, firewalls, new subnets, proxies, and so on. Also, distributed systems are prone to fallacies of distributed computing . On
9916-442: The open-source OpenMM library , which uses a bridge design pattern with two application programming interface (API) levels to interface molecular simulation software to an underlying hardware architecture. With the addition of hardware optimizations, OpenMM-based GPU simulations need no significant modification but achieve performance nearly equal to hand-tuned GPU code, and greatly outperform CPU implementations. Before 2010,
10064-411: The tangent space T Q {\displaystyle TQ} corresponds to the velocities of the points q ∈ Q {\displaystyle q\in Q} , while the cotangent space T ∗ Q {\displaystyle T^{*}Q} corresponds to momenta. (Velocities and momenta can be connected; for the most general, abstract case, this is done with
10212-399: The "coordinator" state. For that, they need some method in order to break the symmetry among them. For example, if each node has unique and comparable identities, then the nodes can compare their identities, and decide that the node with the highest identity is the coordinator. The definition of this problem is often attributed to LeLann, who formalized it as a method to create a new token in
10360-518: The 1960s. The first widespread distributed systems were local-area networks such as Ethernet , which was invented in the 1970s. ARPANET , one of the predecessors of the Internet , was introduced in the late 1960s, and ARPANET e-mail was invented in the early 1970s. E-mail became the most successful application of ARPANET, and it is probably the earliest example of a large-scale distributed application . In addition to ARPANET (and its successor,
10508-609: The 2006 Irving Sigal Young Investigator Award for his simulation results which "have stimulated a re-examination of the meaning of both ensemble and single-molecule measurements, making Pande's efforts pioneering contributions to simulation methodology." Protein misfolding can result in a variety of diseases including Alzheimer's disease, cancer , Creutzfeldt–Jakob disease , cystic fibrosis , Huntington's disease, sickle-cell anemia , and type II diabetes . Cellular infection by viruses such as HIV and influenza also involve folding events on cell membranes . Once protein misfolding
10656-425: The Pande lab introduced a new computing method to measure the topology of its structural changes during fusion. In 2009, researchers used Folding@home to study mutations of influenza hemagglutinin , a protein that attaches a virus to its host cell and assists with viral entry. Mutations to hemagglutinin affect how well the protein binds to a host's cell surface receptor molecules, which determines how infective
10804-554: The ability to efficiently complete many 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 researchers to study biomedical questions formerly considered impractical to tackle computationally. Professional software developers are responsible for most of Folding@home's code, both for the client and server-side. The development team includes programmers from Nvidia , ATI , Sony , and Cauldron Development. Clients can be downloaded only from
10952-436: The aggregate formation, which could aid in the development of therapeutic drug therapies for the disease and greatly assist with experimental nuclear magnetic resonance spectroscopy studies of Aβ oligomers . Later that year, Folding@home began simulations of various Aβ fragments to determine how various natural enzymes affect the structure and folding of Aβ. Huntington's disease is a neurodegenerative genetic disorder that
11100-438: The calculations on the work unit as a background process . A large majority of Folding@home's cores are based on GROMACS , one of the fastest and most popular molecular dynamics software packages, which largely consists of manually optimized assembly language code and hardware optimizations. Although GROMACS is open-source software and there is a cooperative effort between the Pande lab and GROMACS developers, Folding@home uses
11248-485: The case of Folding@home the input data and output result processed by the client-software are both digitally signed, the integrity of work can be verified independently from the integrity of the client software itself. Folding@home uses the Cosm software libraries for networking. Folding@home was launched on October 1, 2000, and was the first distributed computing project aimed at bio-molecular systems. Its first client
11396-419: The case of either multiple computers, or a computer that executes a network of interacting processes: which computational problems can be solved in such a network and how efficiently? However, it is not at all obvious what is meant by "solving a problem" in the case of a concurrent or distributed system: for example, what is the task of the algorithm designer, and what is the concurrent or distributed equivalent of
11544-440: The client, which manages the other software components in the background. Through the client, the user may pause the folding process, open an event log, check the work progress, or view personal statistics. The computer clients run continuously in the background at a very low priority, using idle processing power so that normal computer use is unaffected. The maximum CPU use can be adjusted via client settings. The client connects to
11692-581: The complexity of proteins' conformation or configuration space (the set of possible shapes a protein can take), and limits in computing power, all-atom molecular dynamics simulations have been severely limited in the timescales that they can study. While most proteins typically fold in the order of milliseconds, before 2010, simulations could only reach nanosecond to microsecond timescales. General-purpose supercomputers have been used to simulate protein folding, but such systems are intrinsically costly and typically shared among many research groups. Further, because
11840-557: The computations in kinetic models occur serially, strong scaling of traditional molecular simulations to these architectures is exceptionally difficult. Moreover, as protein folding is a stochastic process (i.e., random) and can statistically vary over time, it is challenging computationally to use long simulations for comprehensive views of the folding process. Protein folding does not occur in one step. Instead, proteins spend most of their folding time, nearly 96% in some cases, waiting in various intermediate conformational states, each
11988-449: The computing reliability of GPGPU consumer-grade hardware was largely unknown, and circumstantial evidence related to the lack of built-in error detection and correction in GPU memory raised reliability concerns. In the first large-scale test of GPU scientific accuracy, a 2010 study of over 20,000 hosts on the Folding@home network detected soft errors in the memory subsystems of two-thirds of
12136-515: The coordinates of the origin of the frame attached to the body, and S O ( 3 ) {\displaystyle \mathrm {SO} (3)} represents the rotation matrices that define the orientation of this frame relative to a ground frame. A configuration of the rigid body is defined by six parameters, three from R 3 {\displaystyle \mathbb {R} ^{3}} and three from S O ( 3 ) {\displaystyle \mathrm {SO} (3)} , and
12284-419: The credit points. This cycle repeats automatically. All work units have associated deadlines, and if this deadline is exceeded, the user may not get credit and the unit will be automatically reissued to another participant. As protein folding occurs serially, and many work units are generated from their predecessors, this allows the overall simulation process to proceed normally if a work unit is not returned after
12432-418: The development of tumors . Analysis of these mutations helps explain the root causes of p53-related cancers. In 2004, Folding@home was used to perform the first molecular dynamics study of the refolding of p53's protein dimer in an all-atom simulation of water . The simulation's results agreed with experimental observations and gave insights into the refolding of the dimer that were formerly unobtainable. This
12580-405: The differences between these binding mechanisms. In 2012, Folding@home assisted with the discovery of a mutant form of IL-2 which is three hundred times more effective in its immune system role but carries fewer side effects. In experiments, this altered form significantly outperformed natural IL-2 in impeding tumor growth. Pharmaceutical companies have expressed interest in the mutant molecule, and
12728-446: The disease. Since 2008, its drug design methods for Alzheimer's disease have been applied to Huntington's. More than half of all known cancers involve mutations of p53 , a tumor suppressor protein present in every cell which regulates the cell cycle and signals for cell death in the event of damage to DNA . Specific mutations in p53 can disrupt these functions, allowing an abnormal cell to continue growing unchecked, resulting in
12876-429: The dynamics of the slow-folding 32- residue NTL9 protein out to 1.52 milliseconds, a timescale consistent with experimental folding rate predictions but a thousand times longer than formerly achieved. The model consisted of many individual trajectories, each two orders of magnitude shorter, and provided an unprecedented level of detail into the protein's energy landscape. In 2010, Folding@home researcher Gregory Bowman
13024-534: The dynamics of the small knottin protein EETI, which can identify carcinomas in imaging scans by binding to surface receptors of cancer cells. Interleukin 2 (IL-2) is a protein that helps T cells of the immune system attack pathogens and tumors. However, its use as a cancer treatment is restricted due to serious side effects such as pulmonary edema . IL-2 binds to these pulmonary cells differently than it does to T cells, so IL-2 research involves understanding
13172-403: The elderly and accounts for more than half of all cases of dementia . Its exact cause remains unknown, but the disease is identified as a protein misfolding disease . Alzheimer's is associated with toxic aggregations of the amyloid beta (Aβ) peptide , caused by Aβ misfolding and clumping together with other Aβ peptides. These Aβ aggregates then grow into significantly larger senile plaques ,
13320-473: The entire project's x86 FLOPS throughput. Native support for Nvidia and AMD graphics cards under Linux was introduced with FahCore 17, which uses OpenCL rather than CUDA. Distributed computing The components of a distributed system communicate and coordinate their actions by passing messages to one another in order to achieve a common goal. Three significant challenges of distributed systems are: maintaining concurrency of components, overcoming
13468-447: The equivalent of nearly eight x86 petaFLOPS. In mid-May 2013, Folding@home attained over seven native petaFLOPS, with the equivalent of 14.87 x86 petaFLOPS. It then reached eight native petaFLOPS on June 21, followed by nine on September 9 of that year, with 17.9 x86 petaFLOPS. On May 11, 2016 Folding@home announced that it was moving towards reaching the 100 x86 petaFLOPS mark. Further use grew from increased awareness and participation in
13616-401: The focus has been on designing a distributed system that solves a given problem. A complementary research problem is studying the properties of a given distributed system. The halting problem is an analogous example from the field of centralised computation: we are given a computer program and the task is to decide whether it halts or runs forever. The halting problem is undecidable in
13764-543: The future of molecular medicine and the rational design of therapeutics , such as expediting and lowering the costs of drug discovery . The goal of the first five years of Folding@home was to make advances in understanding folding, while the current goal is to understand misfolding and related disease, especially Alzheimer's. The simulations run on Folding@home are used in conjunction with laboratory experiments, but researchers can use them to study how folding in vitro differs from folding in native cellular environments. This
13912-452: The general case, and naturally understanding the behaviour of a computer network is at least as hard as understanding the behaviour of one computer. However, there are many interesting special cases that are decidable. In particular, it is possible to reason about the behaviour of a network of finite-state machines. One example is telling whether a given network of interacting (asynchronous and non-deterministic) finite-state machines can reach
14060-483: The global Internet), other early worldwide computer networks included Usenet and FidoNet from the 1980s, both of which were used to support distributed discussion systems. The study of distributed computing became its own branch of computer science in the late 1970s and early 1980s. The first conference in the field, Symposium on Principles of Distributed Computing (PODC), dates back to 1982, and its counterpart International Symposium on Distributed Computing (DISC)
14208-489: The infra cost must be considered. A computer program that runs within a distributed system is called a distributed program , and distributed programming is the process of writing such programs. There are many different types of implementations for the message passing mechanism, including pure HTTP, RPC-like connectors and message queues . Distributed computing also refers to the use of distributed systems to solve computational problems. In distributed computing ,
14356-498: The interest stands as a pilot project compared to Alzheimer 's and Huntington's research. Folding@home is assisting in research towards preventing some viruses , such as influenza and HIV , from recognizing and entering biological cells . In 2011, Folding@home began simulations of the dynamics of the enzyme RNase H , a key component of HIV, to try to design drugs to deactivate it. Folding@home has also been used to study membrane fusion , an essential event for viral infection and
14504-490: The internet, or by the redistribution of binaries by a third-party that have been previously modified either in their binary state (i.e. patched ), or by decompiling and recompiling them after modification. These modifications are possible unless the binary files – and the transport channel – are signed and the recipient person/system is able to verify the digital signature, in which case unwarranted modifications should be detectable, but not always. Either way, since in
14652-456: The issue of the graph family from the design of the coordinator election algorithm was suggested by Korach, Kutten, and Moran. In order to perform coordination, distributed systems employ the concept of coordinators. The coordinator election problem is to choose a process from among a group of processes on different processors in a distributed system to act as the central coordinator. Several central coordinator election algorithms exist. So far
14800-465: The most for the project, which can benefit the folding community and accelerate scientific research. Individual and team statistics are posted on the Folding@home website. If a user does not form a new team, or does not join an existing team, that user automatically becomes part of a "Default" team. This "Default" team has a team number of "0". Statistics are accumulated for this "Default" team as well as for specially named teams. Folding@home software at
14948-478: The most powerful and rapidly growing computing platforms, and many scientists and researchers are pursuing general-purpose computing on graphics processing units (GPGPU). However, GPU hardware is difficult to use for non-graphics tasks and usually requires significant algorithm restructuring and an advanced understanding of the underlying architecture. Such customization is challenging, more so to researchers with limited software development resources. Folding@home uses
15096-433: The motivations of citizen scientists and most of these studies have found that participants are motivated to take part because of altruistic reasons; that is, they want to help scientists and make a contribution to the advancement of their research. Many participants in citizen science have an underlying interest in the topic of the research and gravitate towards projects that are in disciplines of interest to them. Folding@home
15244-456: The official Folding@home website or its commercial partners, and will only interact with Folding@home computer files. They will upload and download data with Folding@home's data servers (over port 8080, with 80 as an alternate), and the communication is verified using 2048-bit digital signatures . While the client's graphical user interface (GUI) is open-source, the client is proprietary software citing security and scientific integrity as
15392-433: The open-source Copernicus software, which is based on Folding@home's MSM and other parallelizing methods and aims to improve the efficiency and scaling of molecular simulations on large computer clusters or supercomputers . Summaries of all scientific findings from Folding@home are posted on the Folding@home website after publication. Alzheimer's disease is an incurable neurodegenerative disease which most often affects
15540-401: The other hand, a well designed distributed system is more scalable, more durable, more changeable and more fine-tuned than a monolithic application deployed on a single machine. According to Marc Brooker: "a system is scalable in the range where marginal cost of additional workload is nearly constant." Serverless technologies fit this definition but the total cost of ownership, and not just
15688-426: The parameterization does not change the mechanical characteristics of the system; all of the different parameterizations ultimately describe the same (six-dimensional) manifold, the same set of possible positions and orientations. Some parameterizations are easier to work with than others, and many important statements can be made by working in a coordinate-free fashion. Examples of coordinate-free statements are that
15836-429: The participation of PlayStation 3 consoles, the Folding@home project officially attained a sustained performance level higher than one native petaFLOPS , becoming the first computing system of any kind to do so. Top500 's fastest supercomputer at the time was BlueGene/L , at 0.280 petaFLOPS. The following year, on May 7, 2008, the project attained a sustained performance level higher than two native petaFLOPS, followed by
15984-527: The performance of modified computers, and this aspect of the hobby is accommodated through the competitive nature of the project. Individuals and teams can compete to see who can process the most computer processing units (CPUs). This latest research on Folding@home involving interview and ethnographic observation of online groups showed that teams of hardware enthusiasts can sometimes work together, sharing best practice with regard to maximizing processing output. Such teams can become communities of practice , with
16132-409: The position of a reference point and the orientation of a coordinate frame attached to a rigid body in three-dimensional space form its configuration space, often denoted R 3 × S O ( 3 ) {\displaystyle \mathbb {R} ^{3}\times \mathrm {SO} (3)} where R 3 {\displaystyle \mathbb {R} ^{3}} represents
16280-617: The potential to expedite and lower the costs of drug discovery. In 2010, Folding@home used MSMs and free energy calculations to predict the native state of the villin protein to within 1.8 angstrom (Å) root mean square deviation (RMSD) from the crystalline structure experimentally determined through X-ray crystallography . This accuracy has implications to future protein structure prediction methods, including for intrinsically unstructured proteins . Scientists have used Folding@home to research drug resistance by studying vancomycin , an antibiotic drug of last resort , and beta-lactamase ,
16428-408: The problem, and inform each node about the solution ( D rounds). On the other hand, if the running time of the algorithm is much smaller than D communication rounds, then the nodes in the network must produce their output without having the possibility to obtain information about distant parts of the network. In other words, the nodes must make globally consistent decisions based on information that
16576-468: The project are freely available for other researchers to use upon request and some can be accessed from the Folding@home website. The Pande lab has collaborated with other molecular dynamics systems such as the Blue Gene supercomputer, and they share Folding@home's key software with other researchers, so that the algorithms which benefited Folding@home may aid other scientific areas. In 2011, they released
16724-557: The project as a result of the COVID-19 pandemic , the system achieved a speed of approximately 1.22 exaflops by late March 2020 and reached 2.43 exaflops by April 12, 2020, making it the world's first exaflop computing system . This level of performance from its large-scale computing network has allowed researchers to run computationally costly atomic-level simulations of protein folding thousands of times longer than formerly achieved. Since its launch on October 1, 2000, Folding@home
16872-542: The project from the coronavirus pandemic in 2020. On March 20, 2020 Folding@home announced via Twitter that it was running with over 470 native petaFLOPS, the equivalent of 958 x86 petaFLOPS. By March 25 it reached 768 petaFLOPS, or 1.5 x86 exaFLOPS, making it the first exaFLOP computing system . As of 11 November 2024, the computing power of Folding@home stands at 16.9 petaFLOPS, or 32.9 x86 petaFLOPS. Similarly to other distributed computing projects, Folding@home quantitatively assesses user computing contributions to
17020-406: The project software on unmodified machines and do take part competitively. By January 2020, the number of users was down to 30,000. However, it is difficult to ascertain what proportion of participants are hardware enthusiasts. Although, according to the project managers, the contribution of the enthusiast community is substantially larger in terms of processing power. Supercomputer FLOPS performance
17168-475: The project through a credit system. All units from a given protein project have uniform base credit, which is determined by benchmarking one or more work units from that project on an official reference machine before the project is released. Each user receives these base points for completing every work unit, though through the use of a passkey they can receive added bonus points for reliably and rapidly completing units which are more demanding computationally or have
17316-480: The proteins Folding@home has studied have increased by a factor of four, while its timescales for protein folding simulations have increased by six orders of magnitude. In 2002, Folding@home used Markov state models to complete approximately a million CPU days of simulations over the span of several months, and in 2011, MSMs parallelized another simulation that required an aggregate 10 million CPU hours of computing. In January 2010, Folding@home used MSMs to simulate
17464-629: The question, then produces an answer and stops. However, there are also problems where the system is required not to stop, including the dining philosophers problem and other similar mutual exclusion problems. In these problems, the distributed system is supposed to continuously coordinate the use of shared resources so that no conflicts or deadlocks occur. There are also fundamental challenges that are unique to distributed computing, for example those related to fault-tolerance . Examples of related problems include consensus problems , Byzantine fault tolerance , and self-stabilisation . Much research
17612-431: The rather abstract notion of the tautological one-form .) For a robotic arm consisting of numerous rigid linkages, the configuration space consists of the location of each linkage (taken to be a rigid body, as in the section above), subject to the constraints of how the linkages are attached to each other, and their allowed range of motion. Thus, for n {\displaystyle n} linkages, one might consider
17760-451: The reasons. However, this rationale of using proprietary software is disputed since while the license could be enforceable in the legal domain retrospectively, it does not practically prevent the modification (also known as patching ) of the executable binary files . Likewise, binary-only distribution does not prevent the malicious modification of executable binary-code, either through a man-in-the-middle attack while being downloaded via
17908-472: The ribosome was determined only as of 2011, and Folding@home has also simulated ribosomal proteins , as many of their functions remain largely unknown. Like other distributed computing projects, Folding@home is an online citizen science project. In these projects non-specialists contribute computer processing power or help to analyze data produced by professional scientists. Participants receive little or no obvious reward. Research has been carried out into
18056-403: The same place as the boundary between parallel and distributed systems (shared memory vs. message passing). In parallel algorithms, yet another resource in addition to time and space is the number of computers. Indeed, often there is a trade-off between the running time and the number of computers: the problem can be solved faster if there are more computers running in parallel (see speedup ). If
18204-431: The same position. In mathematics, in particular in topology, a notion of "restricted" configuration space is mostly used, in which the diagonals, representing "colliding" particles, are removed. The position of a single particle moving in ordinary Euclidean 3-space is defined by the vector q = ( x , y , z ) {\displaystyle q=(x,y,z)} , and therefore its configuration space
18352-476: The search to find the most energetically favorable conformation of the protein, i.e., its native state . Thus, understanding protein folding is critical to understanding what a protein does and how it works, and is considered a holy grail of computational biology . Despite folding occurring within a crowded cellular environment , it typically proceeds smoothly. However, due to a protein's chemical properties or other factors, proteins may misfold , that is, fold down
18500-423: The simplest model of distributed computing is a synchronous system where all nodes operate in a lockstep fashion. This model is commonly known as the LOCAL model. During each communication round , all nodes in parallel (1) receive the latest messages from their neighbours, (2) perform arbitrary local computation, and (3) send new messages to their neighbors. In such systems, a central complexity measure
18648-436: The solvent is treated as a mathematical continuum. The core is separate from the client to enable the scientific methods to be updated automatically without requiring a client update. The cores periodically create calculation checkpoints so that if they are interrupted they can resume work from that point upon startup. A Folding@home participant installs a client program on their personal computer . The user interacts with
18796-453: The sphere S 2 {\displaystyle S^{2}} . In this case, one says that the manifold Q {\displaystyle Q} is the sphere, i.e. Q = S 2 {\displaystyle Q=S^{2}} . For n disconnected, non-interacting point particles, the configuration space is R 3 n {\displaystyle \mathbb {R} ^{3n}} . In general, however, one
18944-607: The spread of cancer. Using Folding@home and working closely with the Center for Protein Folding Machinery, the Pande lab hopes to find a drug which inhibits those chaperones involved in cancerous cells. Researchers are also using Folding@home to study other molecules related to cancer, such as the enzyme Src kinase , and some forms of the engrailed homeodomain : a large protein which may be involved in many diseases, including cancer. In 2011, Folding@home began simulations of
19092-419: The structure of the huntingtin protein aggregate and to predict how it forms, assisting with rational drug design methods to stop the aggregate formation. The N17 fragment of the huntingtin protein accelerates this aggregation, and while there have been several mechanisms proposed, its exact role in this process remains largely unknown. Folding@home has simulated this and other fragments to clarify their roles in
19240-519: The symbol q ˙ = d q / d t {\displaystyle {\dot {q}}=dq/dt} refers to velocities. A particle might be constrained to move on a specific manifold . For example, if the particle is attached to a rigid linkage, free to swing about the origin, it is effectively constrained to lie on a sphere. Its configuration space is the subset of coordinates in R 3 {\displaystyle \mathbb {R} ^{3}} that define points on
19388-432: The task is begun, all network nodes are either unaware which node will serve as the "coordinator" (or leader) of the task, or unable to communicate with the current coordinator. After a coordinator election algorithm has been run, however, each node throughout the network recognizes a particular, unique node as the task coordinator. The network nodes communicate among themselves in order to decide which of them will get into
19536-449: The tested GPUs. These errors strongly correlated to board architecture, though the study concluded that reliable GPU computing was very feasible as long as attention is paid to the hardware traits, such as software-side error detection. The first generation of Folding@home's GPU client (GPU1) was released to the public on October 2, 2006, delivering a 20–30 times speedup for some calculations over its CPU-based GROMACS counterparts. It
19684-463: The three and four native petaFLOPS milestones in August 2008 and September 28, 2008 respectively. On February 18, 2009, Folding@home achieved five native petaFLOPS, and was the first computing project to meet these five levels. In comparison, November 2008's fastest supercomputer was IBM 's Roadrunner at 1.105 petaFLOPS. On November 10, 2011, Folding@home's performance exceeded six native petaFLOPS with
19832-410: The total space [ R 3 × S O ( 3 ) ] n {\displaystyle \left[\mathbb {R} ^{3}\times \mathrm {SO} (3)\right]^{n}} except that all of the various attachments and constraints mean that not every point in this space is reachable. Thus, the configuration space Q {\displaystyle Q} is necessarily
19980-400: The user's end involves three primary components: work units, cores, and a client. A work unit is the protein data that the client is asked to process. Work units are a fraction of the simulation between the states in a Markov model . After the work unit has been downloaded and completely processed by a volunteer's computer, it is returned to Folding@home servers, which then award the volunteer
20128-419: The virus strain is to the host organism. Knowledge of the effects of hemagglutinin mutations assists in the development of antiviral drugs . As of 2012, Folding@home continues to simulate the folding and interactions of hemagglutinin, complementing experimental studies at the University of Virginia . In March 2020, Folding@home launched a program to assist researchers around the world who are working on finding
20276-449: The volunteered machines each receive pieces of a simulation (work units), complete them, and return them to the project's database servers , where the units are compiled into an overall simulation. Volunteers can track their contributions on the Folding@home website, which makes volunteers' participation competitive and encourages long-term involvement. Folding@home is one of the world's fastest computing systems. With heightened interest in
20424-472: The wrong pathway and end up misshapen. Unless cellular mechanisms can destroy or refold misfolded proteins, they can subsequently aggregate and cause a variety of debilitating diseases. Laboratory experiments studying these processes can be limited in scope and atomic detail, leading scientists to use physics-based computing models that, when complementing experiments, seek to provide a more complete picture of protein folding, misfolding, and aggregation. Due to
20572-676: Was a screensaver , which would run while the computer was not otherwise in use. In 2004, the Pande lab collaborated with David P. Anderson to test a supplemental client on the open-source BOINC framework. This client was released to closed beta in April 2005; however, the method became unworkable and was shelved in June 2006. The specialized hardware of graphics processing units (GPU) is designed to accelerate rendering of 3-D graphics applications such as video games and can significantly outperform CPUs for some types of calculations. GPUs are one of
20720-589: Was a major source of molecular interactions within the structure. The study helped prepare the Pande lab for future aggregation studies and for further research to find a small peptide which may stabilize the aggregation process. In December 2008, Folding@home found several small drug candidates which appear to inhibit the toxicity of Aβ aggregates. In 2010, in close cooperation with the Center for Protein Folding Machinery, these drug leads began to be tested on biological tissue . In 2011, Folding@home completed simulations of several mutations of Aβ that appear to stabilize
20868-626: Was awarded the Thomas Kuhn Paradigm Shift Award from the American Chemical Society for the development of the open-source MSMBuilder software and for attaining quantitative agreement between theory and experiment. For his work, Pande was awarded the 2012 Michael and Kate Bárány Award for Young Investigators for "developing field-defining and field-changing computational methods to produce leading theoretical models for protein and RNA folding", and
21016-540: Was first held in Ottawa in 1985 as the International Workshop on Distributed Algorithms on Graphs. Various hardware and software architectures are used for distributed computing. At a lower level, it is necessary to interconnect multiple CPUs with some sort of network, regardless of whether that network is printed onto a circuit board or made up of loosely coupled devices and cables. At a higher level, it
21164-462: Was involved in the production of 226 scientific research papers . Results from the project's simulations agree well with experiments. Proteins are an essential component to many biological functions and participate in virtually all processes within biological cells . They often act as enzymes , performing biochemical reactions including cell signaling , molecular transportation, and cellular regulation . As structural elements, some proteins act as
21312-532: Was more stable, efficient, and flexibile in its scientific abilities, and used OpenMM on top of an OpenCL framework. Although these GPU3 clients did not natively support the operating systems Linux and macOS , Linux users with Nvidia graphics cards were able to run them through the Wine software application. GPUs remain Folding@home's most powerful platform in FLOPS . As of November 2012, GPU clients account for 87% of
21460-421: Was officially retired on June 6. 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. Following this, the third generation of Folding@home's GPU client (GPU3) was released on May 25, 2010. While backward compatible with GPU2, GPU3
21608-423: Was originally presented as a parallel algorithm, but the same technique can also be used directly as a distributed algorithm. Moreover, a parallel algorithm can be implemented either in a parallel system (using shared memory) or in a distributed system (using message passing). The traditional boundary between parallel and distributed algorithms (choose a suitable network vs. run in any given network) does not lie in
21756-461: Was the first peer reviewed publication on cancer from a distributed computing project. The following year, Folding@home powered a new method to identify the amino acids crucial for the stability of a given protein, which was then used to study mutations of p53. The method was reasonably successful in identifying cancer-promoting mutations and determined the effects of specific mutations which could not otherwise be measured experimentally. Folding@home
21904-400: Was the first time GPUs had been used for either distributed computing or major molecular dynamics calculations. GPU1 gave researchers significant knowledge and experience with the development of GPGPU software, but in response to scientific inaccuracies with DirectX , on April 10, 2008, it was succeeded by GPU2, the second generation of the client. Following the introduction of GPU2, GPU1
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