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Data-Level Parallelism

Data-level parallelism is an approach to computer processing that aims to increase data throughput by operating on multiple elements of data simultaneously.

There are many motivations for data-level parallelism, including:

  • Researching faster computer systems
  • Multimedia applications
  • Big data applications
Data-Level Parallelism
Lesson 1 of 1
  1. 1
    In computer architecture, a pipeline is associated with instruction-level parallelism in which multiple instructions can be processed simultaneously. As the computing landscape moves toward data-in…
  2. 2
    Imagine you’re a dog walker who has four clients who need their dogs walked in the afternoon. You start your first day walking the first dog. You return with the first and start walking the second….
  3. 3
    Vector processing was conceived in the 1960s and is one of the earliest applications of SIMD computing. Instead of processing one value at a time using a single instruction, researchers were lookin…
  4. 4
    So how is a vector architecture different from a scalar one? ##### Vector Registers To operate on large amounts of data, the CPU will need somewhere to put it. Vector registers are just like regul…
  5. 5
    Processors used for personal and business computers started out as scalar processors where the registers and functional units were meant for single elements of data. Because of higher demand, data …
  6. 6
    As long as there was a need for visual output on a computer, Graphical Processing Units (GPUs) existed. They provide the specific function of handling all the information to be output to the user…
  7. 7
    Great work finishing the topics on Data-Level Parallelism. In this lesson you covered: - SIMD architectures and their benefits in data-heavy applications. - Vector processors and their early influe…

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