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There are slight differences between parallel and distributed computing, however, the result is almost the same. As per research, many students pursue computer science course much more than earlier.

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About Parallel and Distributed Computing

Parallel computing is a system, which divides a job into several sub-tasks and them performs them simultaneously for enhancing efficiency and speed. A single problem is divided into multiple parts.


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Every part thereafter is broken down into many instructions. The parts are allocated to various processors that execute them at the same time. It enhances the execution speed of programs.

Distributed computing is a model, which studies the distributed systems. These are systems, which have many computers situated in various locations. The computers in this system work on the same program. This program is divided into many tasks and then allocated to various computers. Here the computers interact with messages. When computing is completed, the result is collated and then presented to users.

The Differences Between Parallel Computing and Distributed Computing

The differences are discussed in our Parallel and Distributed Computing assignment help free from plagiarism as follows:

  1. Parallel computing needs a single computer with many processors. Many processors in the same computer can execute instructions at the same time. The processors work to complete the same job. They need to share data and resources. In a distributed computing, many computer systems are there, The multiple computer systems perform divided tasks.
  2. In parallel computing, tasks are divided into many small parts. The smaller jobs are assigned to many processors. The result of a task can be the input of another. This enhances the dependency among the processors. Parallel computing is tightly coupled. Distributed computing is loosely coupled.
  3. In parallel computing, the number of processors to be added is limited. The reason is that the bus that connects the memory and processors can handle just limited connections. This makes parallel computing less scalable. Distributed computing is more scalable. The reason is that the computers connected to the network communicate through messages.
  4. In a parallel system, the processes have the same master clock regarding synchronization. As all processors have been hosted on the physical system, there is no requirement for synchronization algorithms. In distributed computing, the separate processing systems might not have access to a central clock. In distributed computing, the processing systems cannot access the central clock. Thus they have to implement the synchronization algorithms.


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Where are Parallel and Distributing Computing Used?

Parallel computing is used in places that need faster and greater processing power. The examples are supercomputers. As there are no lags in message passing, the systems have high efficiency and speed. Distributed computing is used if computers are situated at various geographical locations. Speed in these cases is not an important matter. It is a preferred choice if you need scalability. These are discussed in our online help with assignment on Parallel and Distributed Computing topics.

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