Grid computing

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Grid computing is the use of a shared "infrastructure that bonds and unifies globally remote and diverse [computing] resources"[1] for the purposes of completing one or more computational tasks that would otherwise require significantly more time if performed on any single machine. Grid computing is a form of distributed computing whereby a virtual supercomputer is formed from many loosely coupled computers acting together to perform large tasks. For certain applications, 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 network (private, public, or the Internet) via a conventional network interface, such as Ethernet.[1] This is in contrast to the traditional notion of a supercomputer, which has many processors connected by a local high-speed computer bus.

One of the main strategies of grid computing is to use middleware to divide and apportion pieces of a program among several computers, sometimes up to many thousands. Grid computing involves computation in a distributed fashion, which may also involve the aggregation of large-scale clusters, which may vary in size from a small group of workstations confined to a network within a corporation (intra-node cooperation) to large public collaborations across many companies and networks (inter-node cooperation).


The idea grid computing originated in the 1990s as a metaphor for making computer power as easy to access as an electric power grid. Where parallel computing and supercomputers were primarily used in the '80s and '90s, grid computing began to take shape as an option by the mid-1990s. In 1995, the Information-Wide Area Year (I-WAY) project was initiated, dedicated to the integration of other existing high-bandwidth networks and the management of software run over them. This project stood out as one of the first major milestones towards true grid computing.[2][3] Not long afterwards, CPU scavenging and volunteer computing projects like in 1997[4] and SETI@home in 1999[5] began to harness the power of networked PCs worldwide to solve CPU-intensive research problems.

Grid computing was further refined with Ian Foster and Carl Kesselman's widely regarded 1998 work The Grid: Blueprint for a New Computing Infrastructure, in which they set out to define and extend the concepts surrounding the idea.[6] Along with software developer Steven Tuecke, the trio previously lent their expertise to the I-WAY project, particularly through their own Globus Project, which would link sites into "virtual organization" for scientific collaboration.[7] The group would not only further refine the definition in 2002, but they would also lead the release of the Globus Toolkit, an open-source toolkit for grid computing.[7]

In 2007 the term cloud computing came into popularity, which is conceptually similar to the canonical Foster definition of grid computing (in terms of computing resources being consumed as electricity is from the power grid). Indeed, grid computing is often (but not always) associated with the delivery of cloud computing systems.[8]

Grid computing vs. supercomputing

While supercomputing is essentially parallel computing[9], grid computing is a special type of parallel computing that relies on complete computers connected to a network. The primary performance disadvantage of grid computing to supercomputing is that the various processors and local storage areas typically 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.[10]

Additionally, differences in programming and deployment exist. 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 issues.[11] 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 it eliminates complications due to multiple instances of the same program running in the same shared memory and storage space at the same time.

Design considerations

One feature of distributed grids is that they can be formed from computing resources belonging to multiple administrative domains. This can facilitate commercial transactions, as in utility computing, or make it easier to assemble volunteer computing networks. However, the computers which are actually performing the calculations might not be entirely trustworthy, requiring additional security measures to prevent malfunctions or malicious participants from producing false, misleading, or erroneous results. Authentication, authorization, and encryption methods must all be employed to ensure "the integrity and confidentiality of the data processed within the grid."[12]

The impacts of trust and availability on performance and development can influence the choice of whether to deploy onto a dedicated cluster, to idle machines internal to the developing organization, or to open an external network of volunteers or contractors.[12] In many cases, the participating nodes must trust the central system not to abuse the access that is being granted, by interfering with the operation of other programs, mangling stored information, transmitting private data, or creating new security holes. Other systems employ measures to reduce the amount of trust "client" nodes must place in the central system such as placing applications in virtual machines.

Public systems or those crossing administrative domains (including different departments in the same organization) often result in the need to run on heterogeneous systems, using different operating systems and hardware architectures.[12] With many languages, there is a trade off between investment in software development and the number of platforms that can be supported (and thus the size of the resulting network). Cross-platform languages can reduce the need to make this trade off, though potentially at the expense of high performance on any given node (due to run-time interpretation or lack of optimization for the particular platform).[13]

Projects and applications

Grid computing offers a way to solve large-scale problems such as protein folding, financial modeling, and geographic and meteorological simulations. Grids offer a way of using the information technology resources optimally inside an organization. Examples include:

  • As of September 2014 the open-source Berkeley Open Infrastructure for Network Computing (BOINC) platform was being used by over 3.2 million users. Of those users, 1.5 million users were attached to SETI@home, a project dedicated to detecting intelligent life beyond Earth, achieving an average of 1,969 TeraFLOPS (floating point operations per second).[14]
  • As of September 2014, Folding@home, a project dedicated to disease research, achieves 39,990 TeraFLOPS from over 172,000 users.[15]
  • As of September 2014, the Worldwide LHC Computing Grid — dedicated to handling nearly 30 petabytes of data per year from the Large Hadron Collider — involves 40 countries, 170 computing centers, and two million jobs run every day, making it one of the largest grid computing projects ever.[16]

Further reading

See also


This article reuses numerous content elements from the Wikipedia article.


  1. 1.0 1.1 Magoulès, Frédéric (2009). Fundamentals of Grid Computing: Theory, Algorithms and Technologies. CRC Press. pp. 322. ISBN 9781439803684. Retrieved 17 September 2014. 
  2. Berman, Fran; Fox, Geoffrey; Hey, Tony (2003). "Chapter 1: The Grid: Past, Present, and Future". Grid Computing: Making the Global Infrastructure a Reality. John Wiley and Sons. pp. 9–50. ISBN 9780470853191. Retrieved 17 September 2014. 
  3. National Science and Technology Council's Committee on Computing, Information, and Communications (November 1996). High Performance Computing and Communications: Advancing the Frontiers of Information Technology. National Science and Technology Council. Retrieved 17 September 2014. 
  4. " History & Timeline". Retrieved 17 September 2014. 
  5. "About SETI@home". SETI@home. University of California. Retrieved 17 September 2014. 
  6. Foster, Ian (20 July 2002). "What is the Grid? A Three Point Checklist". Retrieved 17 September 2014. 
  7. 7.0 7.1 Braverman, Amy M. (April 2004). "Father of the Grid". The University of Chicago Magazine 96 (4). Retrieved 17 September 2014. 
  8. Myerson, Judith M. (3 March 2009). "Cloud computing versus grid computing". IBM developerWorks. IBM. Retrieved 17 September 2014. 
  9. Lafferty, Eduard L.; Michaud, Marion C.; Prelle, Myra Jean; Goethert, Joan B. (1993). Parallel Computing: An Introduction. Noyes Data Corporation. pp. 146. ISBN 9781437744934. Retrieved 17 September 2014. 
  10. "Computational problems". GridCafé. e-ScienceTalk. Retrieved 17 September 2014. 
  11. Graham, Susan L.; Snir, Marc; Patterson, Cynthia A. (2005). Getting Up To Speed: The Future of Supercomputing. The National Academies Press. pp. 134–135. ISBN 9780309165518. Retrieved 17 September 2014. 
  12. 12.0 12.1 12.2 Jacob, Bart; Brown, Michael; Fukui, Kentaro; Trivedi, Nihar (December 2005) (PDF). Introduction to Grid Computing. IBM. pp. 248. Retrieved 17 September 2014. 
  13. Plaszczak, Pawel; Wellner, Jr., Richard (2005). Grid Computing: The Savvy Manager's Guide. Elsevier. pp. 38–39. ISBN 9780080470764. Retrieved 17 September 2014. 
  14. "Project statistics - SETI@home". All Project Stats. BOINC. Retrieved 17 September 2014. 
  15. "Folding@home Client statistics by OS". Folding@home. Pande Lab, Stanford University. Retrieved 17 September 2014. 
  16. "Welcome - Worldwide LHC Computing Grid". CERN. Retrieved 17 September 2014.