Grid computing

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Grid computing (or the use of computational grids) is the combination of computer resources from multiple administrative domains applied to a common task, usually to a scientific, technical or business problem that requires a great number of computer processing cycles or the need to process large amounts of data.
One of the main strategies of grid computing is using software to divide andapportion pieces of a program among several computers, sometimes up to many thousands. Grid computing is distributed, large-scale cluster computing, as well as a form of network-distributed parallel processing [1]. The size of grid computing may vary from being small — confined to a network of computer workstations within a corporation, for example — to being large, public collaboration across manycompanies and networks. "The notion of a confined grid may also be known as an intra-nodes cooperation whilst the notion of a larger, wider grid may thus refer to an inter-nodes cooperation".[2] This inter-/intra-nodes cooperation "across cyber-based collaborative organizations are also known as Virtual Organizations".[3]
It is a form of distributed computing whereby a “super and virtual computer” iscomposed of a cluster of networked loosely coupled computers acting in concert to perform very large tasks. 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 dataprocessing in support of e-commerce and Web services.

What distinguishes grid computing from conventional cluster computing systems is that grids tend to be more loosely coupled, heterogeneous, and geographically dispersed. Also, while a computing grid may be dedicated to a specialized application, it is often constructed with the aid of general-purpose grid software libraries and middleware.
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Grids versus conventional supercomputers

“Distributed” or “grid” computing in general is a special type of parallel computing that relies on complete computers (with onboard CPU, storage, power supply, network interface, etc.) connected to a network (private, public or the Internet) by a conventional network interface, such as Ethernet. This is in contrast to the traditional notion of asupercomputer, which has many processors connected by a local high-speed computer bus.
The primary advantage of distributed computing is that each node can be purchased as commodity hardware, which when combined can produce similar computing resources to a multiprocessor supercomputer, but at lower cost. This is due to the economies of scale of producing commodity hardware, compared to the lowerefficiency 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 betweenprocessors.
The high-end scalability of geographically dispersed grids is generally favorable, due to the low need for connectivity between nodes relative to the capacity of the public Internet.
There are also some differences in programming and deployment. It can be costly and difficult to write programs so that they can be run in the environment of a supercomputer, which may have a custom operatingsystem, or require the program to address concurrency issues. If a problem can be adequately parallelized, a “thin” layer of “grid” infrastructure can allow conventional, standalone programs to run on multiple machines (but each given a different part of the same problem). This makes it possible to write and debug on a single conventional machine, and eliminates complications due to multiple...
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