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  2. Distributed data processing - Wikipedia

    en.wikipedia.org/wiki/Distributed_data_processing

    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]

  3. Multiple instruction, multiple data - Wikipedia

    en.wikipedia.org/wiki/Multiple_instruction...

    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.

  4. Distributed computing - Wikipedia

    en.wikipedia.org/wiki/Distributed_computing

    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 ...

  5. Distributed Data Management Architecture - Wikipedia

    en.wikipedia.org/wiki/Distributed_Data...

    Distributed Data Management Architecture (DDM) is IBM's open, published software architecture for creating, managing and accessing data on a remote computer. DDM was initially designed to support record-oriented files; it was extended to support hierarchical directories, stream-oriented files, queues, and system command processing; it was further extended to be the base of IBM's Distributed ...

  6. Apache Hadoop - Wikipedia

    en.wikipedia.org/wiki/Apache_Hadoop

    Apache Hadoop (/ h ə ˈ d uː p /) is a collection of open-source software utilities for reliable, scalable, distributed computing.It provides a software framework for distributed storage and processing of big data using the MapReduce programming model.

  7. Stream processing - Wikipedia

    en.wikipedia.org/wiki/Stream_processing

    Data locality is a specific type of temporal locality common in signal and media processing applications where data is produced once, read once or twice later in the application, and never read again. Intermediate streams passed between kernels as well as intermediate data within kernel functions can capture this locality directly using the ...

  8. Distributed algorithm - Wikipedia

    en.wikipedia.org/wiki/Distributed_algorithm

    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 .

  9. Distributed networking - Wikipedia

    en.wikipedia.org/wiki/Distributed_Networking

    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 ...