Parallelism in computer architecture and organisation

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parallelism in computer architecture and organisation

Computer Architecture and Parallel Processing by Kai Hwang

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Published 17.12.2018

Parallel Processing

Parallel Computer Architecture - Quick Guide

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This unit will address several advanced topics in computer architecture, focusing on the reasons for and the consequences of the recent switch from sequential processing to parallel processing by hardware producers. You will learn that parallel programming is not easy and that parallel processing imposes certain limitations in performance gains, as seen in the well-known Amdahl's law. You will also look into the concepts of shared memory multiprocessing and cluster processing as two common means of improving performance with parallelism. The unit will conclude with a look at some of the programming techniques used in the context of parallel machines. Read section 1. These two pages give a summary of processor and chip trends to overcome the challenge of increasing performance and addressing the heat problem of a single core. Read sections 2.

Instead of processing each instruction sequentially, a parallel processing system provides concurrent data processing to increase the execution time. In this the system may have two or more ALU's and should be able to execute two or more instructions at the same time. The purpose of parallel processing is to speed up the computer processing capability and increase its throughput. NOTE: Throughput is the number of instructions that can be executed in a unit of time. Parallel processing can be viewed from various levels of complexity. At the lowest level, we distinguish between parallel and serial operations by the type of registers used. At the higher level of complexity, parallel processing can be achieved by using multiple functional units that perform many operations simultaneously.

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Computer Architecture - Introduction to Parallelism

Computer software were written conventionally for serial computing. This meant that to solve a problem, an algorithm divides the problem into smaller instructions. These discrete instructions are then executed on Central Processing Unit of a computer one by one. Only after one instruction is finished, next one starts. Real life example of this would be people standing in a queue waiting for movie ticket and there is only cashier.

5 thoughts on “Computer Architecture and Parallel Processing by Kai Hwang

  1. Parallel computing is a type of computation in which many calculations or the execution of Specialized parallel computer architectures are sometimes used alongside traditional processors, for accelerating specific tasks. is generally cited as the end of frequency scaling as the dominant computer architecture paradigm.

  2. In the last 50 years, there has been huge developments in the performance and capability of a computer system.

  3. Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously.

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