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Distributed networking, used in distributed computing, is the network system over which computer programming, software, and its data are spread out across more than one computer, but communicate complex messages through their nodes (computers), and are dependent upon each other. The goal of a distributed network is to share resources, typically ...
In computing, multiple instruction, multiple data (MIMD) is a technique employed to achieve parallelism. Machines using MIMD have a number of processor cores that function asynchronously and independently. At any time, different processors may be executing different instructions on different pieces of data.
A distributed algorithm is an algorithm designed to run on computer hardware constructed from interconnected processors. Distributed algorithms are used in different application areas of distributed computing , such as telecommunications , scientific computing , distributed information processing , and real-time process control .
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. [34] Client–server: architectures where smart clients contact the server for data then format and display it to the users. Input at the client is committed ...
A distributed OS provides the essential services and functionality required of an OS but adds attributes and particular configurations to allow it to support additional requirements such as increased scale and availability. To a user, a distributed OS works in a manner similar to a single-node, monolithic operating system. That is, although it ...
BCG (Binary Coded Graphs) is both a file format for storing very large graphs on disk (using efficient compression techniques) and a software environment for handling this format, including partitioning graphs for distributed processing. BCG also plays a key role in CADP as many tools rely on this format for their inputs/outputs.
Distributed data processing [1] (DDP) [2] was the term that IBM used for the IBM 3790 (1975) and its successor, the IBM 8100 (1979). Datamation described the 3790 in March 1979 as "less than successful." [3] [4] Distributed data processing was used by IBM to refer to two environments: IMS DB/DC; CICS/DL/I [5] [6]
As an example, if a problem can be described as a pipeline where data x is processed subsequently through functions f, g, h, etc. (the result is h(g(f(x)))), then this can be expressed as a distributed memory problem where the data is transmitted first to the node that performs f that passes the result onto the second node that computes g, and ...