What is Vector Processing in Computer Architecture?

Vector processing is a central processing unit that can perform the complete vector input in individual instruction. It is a complete unit of hardware resources that implements a sequential set of similar data elements in the memory using individual instruction.

The scientific and research computations involve many computations which require extensive and high-power computers. These computations when run in a conventional computer may take days or weeks to complete. The science and engineering problems can be specified in methods of vectors and matrices using vector processing.

Features of Vector Processing

There are various features of Vector Processing which are as follows −

  • A vector is a structured set of elements. The elements in a vector are scalar quantities. A vector operand includes an ordered set of n elements, where n is known as the length of the vector.

  • Each clock period processes two successive pairs of elements. During one single clock period, the dual vector pipes and the dual sets of vector functional units allow the processing of two pairs of elements.

    As the completion of each pair of operations takes place, the results are delivered to appropriate elements of the result register. The operation continues just before the various elements processed are similar to the count particularized by the vector length register.

  • In parallel vector processing, more than two results are generated per clock cycle. The parallel vector operations are automatically started under the following two circumstances −

    • When successive vector instructions facilitate different functional units and multiple vector registers.
    • When successive vector instructions use the resulting flow from one vector register as the operand of another operation utilizing a different functional unit. This phase is known as chaining.
  • A vector processor implements better with higher vectors because of the foundation delay in a pipeline.

  • Vector processing decrease the overhead related to maintenance of the loop-control variables which creates it more efficient than scalar processing.

Updated on: 01-Nov-2023

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