Vector Processing
A Vector Processing computer capabilities are demanded in only specialized applications. thus, it is a class of computational problems that are beyond the capabilities of a conventional computer. These problems requires vast number of computations that will take a conventional computer days or even weeks to complete. In many science and engineering applications, the problems can be formulated in terms of vectors and matrices that are accepted to vector processing. The following are the representative applications areas where vector processing is important:
Long-range weather forecasting
Petroleum exploration
Seismic data analysis
Medical diagnosis
Image processing
Aerodynamic and space flight simulations
AI & expert systems
Mapping the human genome
To reach the required level of high performance, it is necessary to utilize the fastest and most reliable hardware and apply innovative procedures where vector processing is importance.
Array Processor
An array processor is a processor that performs calculations on large arrays of data. the term is used to refer to two different types of processors:
1. Attached Array Processor
It is a helping element processor attached to a general purpose computer which is intended to improve the performance of the host computer in specific numerical calculation task. Array processing are used in following applications:
Radar system
Wireless communication
Seismic data analysis
2. SIMD Array Processor
it is a processor that has a single instructions multiple data organization. it manipulates vector instruction by means of multiple functional units responding to a common instruction. SIMD Array Processor are used in:
3D Graphics processing
3D video/audio processing
Scalar Processing
A scalar processing operates instruction fetch one at a time. Scalar Processor start a computer in a much shorter time since only one tasks are being executed at a time. The Intel 486 is an example of scalar processor when compare to vector processor a single instruction operates simultaneously on multiple data items. i.e. it is also classified as single-instruction single-data (SISD) machines.
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