Grid computing: Difference between revisions

Revision as of 17:22, 20 June 2023

Revision as of 14:28, 7 July 2023

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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 inbetween programming and MC.{{clarify|date=July 2019}} It can be costly and difficult to write programs that can run in the environment of a supercomputer, which may have a custom operating system, or require the program to address [[Concurrency (computer science)|concurrency]] issues. If a problem can be adequately parallelized, a “thin” layer of “grid” infrastructure can allow conventional, standalone programs, given a different part of the same problem, to run on multiple machines. This makes it possible to write and debug on a single conventional machine and eliminates complications due to multiple instances of the same program running in the same shared [[computer memory|memory]] and storage space at the same time.

==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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==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 }}</ref> Grids offer a way of using the information technology resources optimally inside an organization. They also provide a means for offering information technology as a [[utility computing|utility]] for commercial and noncommercial clients, with those clients paying only for what they use, as with electricity or water.

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" />