Parallel Processing and Computer Architecture: An Overview, Study notes of Computer Architecture and Organization

An overview of parallel processing techniques and computer architecture classifications. It explains how parallel processing enhances computer processing capabilities by enabling simultaneous data-processing tasks. The document also details flynn's classification of computer architectures, including sisd, simd, misd, and mimd, with examples illustrating their functionalities. This resource is valuable for understanding the principles and applications of parallel processing in computer systems, offering insights into how different architectures handle instruction and data streams to improve computational speed and efficiency. It is suitable for students and professionals in computer science and engineering.

Typology: Study notes

2021/2022

Available from 12/24/2025

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Parallel Processing
Parallel processing can be described as a class of techniques which enables the system
to achieve simultaneous data-processing tasks to increase the computational speed of a
computer system.
A parallel processing system can carry out simultaneous data-processing to achieve
faster execution time. For instance, while an instruction is being processed in the ALU
component of the CPU, the next instruction can be read from memory.
The primary purpose of parallel processing is to enhance the computer processing
capability and increase its throughput, i.e. the amount of processing that can be
accomplished during a given interval of time.
A parallel processing system can be achieved by having a multiplicity of functional units
that perform identical or different operations simultaneously. The data can be
distributed among various multiple functional units.
The following diagram shows one possible way of separating the execution unit into
eight functional units operating in parallel.
The operation performed in each functional unit is indicated in each block if the
diagram:
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Parallel Processing

Parallel processing can be described as a class of techniques which enables the system to achieve simultaneous data-processing tasks to increase the computational speed of a computer system. A parallel processing system can carry out simultaneous data-processing to achieve faster execution time. For instance, while an instruction is being processed in the ALU component of the CPU, the next instruction can be read from memory. The primary purpose of parallel processing is to enhance the computer processing capability and increase its throughput, i.e. the amount of processing that can be accomplished during a given interval of time. A parallel processing system can be achieved by having a multiplicity of functional units that perform identical or different operations simultaneously. The data can be distributed among various multiple functional units. The following diagram shows one possible way of separating the execution unit into eight functional units operating in parallel. The operation performed in each functional unit is indicated in each block if the diagram:

o The adder and integer multiplier performs the arithmetic operation with integer numbers. o The floating-point operations are separated into three circuits operating in parallel. o The logic, shift, and increment operations can be performed concurrently on different data. All units are independent of each other, so one number can be shifted while another number is being incremented.

Classification of computer architecture

According to Flynn's there are four different classification of computer architecture,

1) SISD (Single Instruction Single Data Stream)

MISD (Multiple Instruction Single Data stream)

An MISD computing is a multiprocessor machine capable of executing different instructions on processing elements but all of them operating on the same data set.

MIMD (Multiple Instruction Multiple Data Stream)

A MIMD system is a multiprocessor machine that is capable of executing multiple instructions over multiple data streams. Each processing element has a separate instruction stream and data stream.