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{{Distinguish|Cluster computing}}
{{More footnotes needed|date=April 2025}}
'''Grid computing''' is the use of widely distributed [[computer]] [[System resource|resources]] to reach a common goal. A computing grid can be thought of as a [[distributed system]] with non-interactive workloads that involve many files. Grid computing is distinguished from conventional high-performance computing systems such as [[Cluster (computing)|cluster]] computing in that grid computers have each node set to perform a different task/application. Grid computers also tend to be more [[heterogeneous]] and geographically dispersed (thus not physically coupled) than cluster computers.<ref>[http://www.e-sciencecity.org/EN/gridcafe/what-is-the-grid.html What is grid computing? - Gridcafe] {{Webarchive|url=https://web.archive.org/web/20130210072831/http://www.e-sciencecity.org/EN/gridcafe/what-is-the-grid.html |date=2013-02-10 }}. E-sciencecity.org. Retrieved 2013-09-18.</ref> Although a single grid can be dedicated to a particular application, commonly a grid is used for a variety of purposes. Grids are often constructed with general-purpose grid [[middleware]] software libraries. Grid sizes can be quite large.<ref>{{cite web |url=
Grids are a form of [[distributed computing]] composed of many networked [[Loose coupling|loosely coupled]] computers acting together to perform large tasks. For certain applications, distributed or grid computing can be seen as a special type of [[parallel computing]] that relies on complete computers (with onboard CPUs, storage, power supplies, network interfaces, etc.) connected to a [[computer network]] (private or public) by a conventional [[Network interface controller|network interface]], such as [[Ethernet]]. This is in contrast to the traditional notion of a [[supercomputer]], which has many processors connected by a local high-speed [[computer bus]]. This technology has been applied to computationally intensive scientific, mathematical, and academic problems through [[volunteer computing]], and it is used in commercial enterprises for such diverse applications as [[drug discovery]], [[economic forecasting]], [[seismic analysis]], and [[back office]] data processing in support for [[e-commerce]] and [[Web service]]s.
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“Distributed” or “grid” computing in general is a special type of [[parallel computing]] that relies on complete computers (with onboard CPUs, storage, power supplies, network interfaces, etc.) connected to a [[computer network|network]] (private, public or the [[Internet]]) by a conventional [[Network interface controller|network interface]] producing commodity hardware, compared to the lower efficiency of designing and constructing a small number of custom supercomputers. The primary performance disadvantage is that the various processors and local storage areas do not have high-speed connections. This arrangement is thus well-suited to applications in which multiple parallel computations can take place independently, without the need to communicate intermediate results between processors.<ref>[http://www.e-sciencecity.org/EN/gridcafe/computational-problems.html Computational problems - Gridcafe] {{Webarchive|url=https://web.archive.org/web/20120825003633/http://www.e-sciencecity.org/EN/gridcafe/computational-problems.html |date=2012-08-25 }}. E-sciencecity.org. Retrieved 2013-09-18.</ref> The high-end [[scalability]] of geographically dispersed grids is generally favorable, due to the low need for connectivity between [[Node (computer science)|nodes]] relative to the capacity of the public Internet.<ref>{{Cite web |title=What is grid computing? |url=https://www.ionos.com/digitalguide/server/know-how/grid-computing/ |access-date=2022-03-23 |website=IONOS Digitalguide |language=en |archive-date=2022-01-28 |archive-url=https://web.archive.org/web/20220128110848/https://www.ionos.com/digitalguide/server/know-how/grid-computing/ |url-status=live }}</ref>
There are also some differences
==Design considerations and variations==
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One feature of distributed grids is that they can be formed from computing resources belonging to one or multiple individuals or organizations (known as multiple [[administrative ___domain]]s). This can facilitate commercial transactions, as in [[utility computing]], or make it easier to assemble [[volunteer computing]] networks.
One disadvantage of this feature is that the computers which are actually performing the calculations might not be entirely trustworthy. The designers of the system must thus introduce measures to prevent malfunctions or malicious participants from producing false, misleading, or erroneous results, and from using the system as an attack vector. This often involves assigning work randomly to different nodes (presumably with different owners) and checking that at least two different nodes report the same answer for a given work unit. Discrepancies would identify malfunctioning and malicious nodes. However, due to the lack of central control over the hardware, there is no way to guarantee that [[Node (computer science)|nodes]] will not drop out of the network at random times. Some nodes (like laptops or [[dial-up]] Internet customers) may also be available for computation but not network communications for unpredictable periods. These variations can be accommodated by assigning large work units (thus reducing the need for continuous network connectivity) and reassigning work units when a given node fails to report its results in the expected time.
Another set of what could be termed social compatibility issues in the early days of grid computing related to the goals of grid developers to carry their innovation beyond the original field of high-performance computing and across disciplinary boundaries into new fields, like that of high-energy physics.<ref>{{Cite journal|last1=Kertcher|first1=Zack|last2=Coslor|first2=Erica|date=2018-07-10|title=Boundary Objects and the Technical Culture Divide: Successful Practices for Voluntary Innovation Teams Crossing Scientific and Professional Fields|journal=Journal of Management Inquiry|volume=29|language=en|pages=76–91|doi=10.1177/1056492618783875|issn=1056-4926|hdl=11343/212143|s2cid=149911242|url=http://minerva-access.unimelb.edu.au/bitstream/11343/212143/5/Kertcher%20%26%20Coslor%20-%20Boundary%20Objects%20and%20the%20Technical%20Culture%20Divide%202018-02-13.pdf|doi-access=free|access-date=2019-09-18|archive-date=2022-03-28|archive-url=https://web.archive.org/web/20220328181127/https://minerva-access.unimelb.edu.au/bitstream/11343/212143/5/Kertcher%20%26%20Coslor%20-%20Boundary%20Objects%20and%20the%20Technical%20Culture%20Divide%202018-02-13.pdf|url-status=live}}</ref>
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===Progress===
In November 2006, [[Edward Seidel]] received the [[Sidney Fernbach Award]] at the Supercomputing Conference in [[Tampa, Florida]].<ref>{{cite web|title=Edward Seidel 2006 Sidney Fernbach Award Recipient|url=http://www.computer.org/portal/web/awards/seidel|work=IEEE Computer Society Awards|publisher=IEEE Computer Society|access-date=14 October 2011|archive-url=https://web.archive.org/web/20110815212928/http://www.computer.org/portal/web/awards/seidel|archive-date=15 August 2011|url-status=dead}}</ref> "For outstanding contributions to the development of software for HPC and Grid computing to enable the collaborative numerical investigation of complex problems in physics; in particular, modeling black hole collisions."<ref>{{cite web|url=http://www.computer.org/portal/web/awards/seidel|title=Edward Seidel • IEEE Computer Society|website=www.computer.org|access-date=14 March 2018|archive-url=https://web.archive.org/web/20110815212928/http://www.computer.org/portal/web/awards/seidel|archive-date=15 August 2011|url-status=dead}}</ref> This award, which is one of the highest honors in computing, was awarded for his achievements in numerical relativity.
==Fastest virtual supercomputers==
* As of March 2020, [[Folding@home]] – 1.1 exaFLOPS.<ref name="FAH osstats2">{{cite web |author=Pande lab |title=Client Statistics by OS |url=https://stats.foldingathome.org/os |url-status=live |archive-url=https://archive.
* As of April 7, 2020, [[BOINC]] – 29.8 PFLOPS.<ref name="BoincStats">{{cite web|url=http://boincstats.com/en/stats/-1/project/detail|title=BOINCstats – BOINC combined credit overview|access-date=October 30, 2016|archive-date=January 22, 2013|archive-url=https://web.archive.org/web/20130122022019/http://boincstats.com/en/stats/-1/project/detail/|url-status=live}}</ref>
* As of November 2019, IceCube via OSG – 350 fp32 PFLOPS.<ref>{{cite web|url=https://www.sdsc.edu/News%20Items/PR20191119_GPU_Cloudburst.html|title=SDSC, Wisconsin U IceCube Center Conduct GPU Cloudburst Experiment|publisher=SDSC|access-date=April 22, 2022|archive-date=September 14, 2022|archive-url=https://web.archive.org/web/20220914153408/https://www.sdsc.edu/News%20Items/PR20191119_GPU_Cloudburst.html|url-status=live}}</ref>
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==Projects and applications==
Grid computing offers a way to solve [[Grand Challenge problem]]s such as [[protein folding]], financial [[model (abstract)|modeling]], [[earthquake]] simulation, and [[climate]]/[[weather]] modeling, and was integral in enabling the Large Hadron Collider at CERN.<ref>{{cite journal |last1=Kertcher |first1=Zack |last2=Venkatraman |first2=Rohan |last3=Coslor |first3=Erica |title=Pleasingly parallel: Early cross-disciplinary work for innovation diffusion across boundaries in grid computing |journal=Journal of Business Research |date=23 April 2020 |volume=116 |pages=581–594 |doi=10.1016/j.jbusres.2020.04.018 |s2cid=219048576 |hdl=11343/237477 |hdl-access=free }}</ref> Grids offer a way of using
As of October 2016, over 4 million machines running the open-source [[Berkeley Open Infrastructure for Network Computing]] (BOINC) platform are members of the [[World Community Grid]].<ref name="BoincStats" /> One of the projects using BOINC is [[SETI@home]], which was using more than 400,000 computers to achieve 0.828 [[FLOPS|TFLOPS]] as of October 2016. As of October 2016 [[Folding@home]], which is not part of BOINC, achieved more than 101 x86-equivalent petaflops on over 110,000 machines.<ref name="FAH osstats2" />
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The [[European Union]] funded projects through the [[framework programme]]s of the [[European Commission]]. [[BEinGRID]] (Business Experiments in Grid) was a research project funded by the European Commission<ref>{{cite web|url=http://www.beingrid.eu/|title=beingrid.eu: Stromkosten Vergleiche -|website=beingrid.eu: Stromkosten Vergleiche|access-date=14 March 2018|archive-url=https://web.archive.org/web/20110723100417/http://www.beingrid.eu/|archive-date=23 July 2011|url-status=dead}}</ref> as an [[Integrated Project (EU)|Integrated Project]] under the [[Sixth Framework Programme]] (FP6) sponsorship program. Started on June 1, 2006, the project ran 42 months, until November 2009. The project was coordinated by [[Atos Origin]]. According to the project fact sheet, their mission is “to establish effective routes to foster the adoption of grid computing across the EU and to stimulate research into innovative business models using Grid technologies”. To extract best practice and common themes from the experimental implementations, two groups of consultants are analyzing a series of pilots, one technical, one business. The project is significant not only for its long duration but also for its budget, which at 24.8 million Euros, is the largest of any FP6 integrated project. Of this, 15.7 million is provided by the European Commission and the remainder by its 98 contributing partner companies. Since the end of the project, the results of BEinGRID have been taken up and carried forward by [[IT-Tude.com]].
The Enabling Grids for E-sciencE project, based in the [[European Union]] and included sites in Asia and the United States, was a follow-up project to the European DataGrid (EDG) and evolved into the [[European Grid Infrastructure]]. This, along with the [[Worldwide LHC Computing Grid]]<ref>{{cite web|url=http://wlcg.web.cern.ch/|title=Welcome to the Worldwide LHC Computing Grid - WLCG|website=wlcg.web.cern.ch|access-date=14 March 2018|archive-date=25 July 2018|archive-url=https://web.archive.org/web/20180725112849/http://wlcg.web.cern.ch/|url-status=live}}</ref> (
The [[distributed.net]] project was started in 1997.
The [[NASA Advanced Supercomputing facility]] (NAS) ran [[genetic algorithm]]s using the [[Condor cycle scavenger]] running on about 350 [[Sun Microsystems]] and [[Silicon Graphics|SGI]] workstations.
In 2001, [[United Devices]] operated the [[United Devices Cancer Research Project]] based on its [[Grid MP]] product, which cycle-scavenges on volunteer PCs connected to the Internet. The project ran on about 3.1 million machines before its close in 2007.<ref>
===Definitions===
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* Plaszczak/Wellner<ref>P Plaszczak, R Wellner, ''Grid computing'', 2005, Elsevier/Morgan Kaufmann, San Francisco</ref> define grid technology as "the technology that enables resource virtualization, on-demand provisioning, and service (resource) sharing between organizations."
* IBM defines grid computing as “the ability, using a set of open standards and protocols, to gain access to applications and data, processing power, storage capacity and a vast array of other computing resources over the Internet. A grid is a type of parallel and distributed system that enables the sharing, selection, and aggregation of resources distributed across ‘multiple’ administrative domains based on their (resources) availability, capacity, performance, cost and users' quality-of-service requirements”.<ref>IBM Solutions Grid for Business Partners: Helping IBM Business Partners to Grid-enable applications for the next phase of e-business on demand</ref>
* An earlier example of the notion of computing as
* Buyya/Venugopal<ref>{{cite web|url=http://www.buyya.com/papers/GridIntro-CSI2005.pdf|title=A Gentle Introduction to Grid Computing and Technologies|access-date=May 6, 2005|archive-date=March 24, 2006|archive-url=https://web.archive.org/web/20060324161402/http://www.buyya.com/papers/GridIntro-CSI2005.pdf|url-status=live}}</ref> define grid as "a type of parallel and distributed system that enables the sharing, selection, and aggregation of geographically distributed [[Wiktionary:autonomy|autonomous]] resources dynamically at runtime depending on their availability, capability, performance, cost, and users' quality-of-service requirements".
==See also==
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* [[Grid Security Infrastructure|Grid Security Infrastructure (GSI)]]
* [[Open Grid Services Architecture|Open Grid Services Architecture (OGSA)]]
* [[Common Object Request Broker Architecture|Common Object Request Broker Architecture (
* [[Open Grid Services Infrastructure|Open Grid Services Infrastructure (OGSI)]]
* [[SAGA (computing)|A Simple API for Grid Applications (SAGA)]]
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{{Parallel Computing|state=collapsed}}
{{Computer sizes|state=collapsed}}
{{Authority control}}
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