What is the Difference Between Parallel and Distributed Computing?

Parallel and distributed computing are related but distinct technologies used to improve computational performance. Here are the key differences between the two:

Number of Computers Required: Parallel computing typically requires one computer with multiple processors, while distributed computing involves several autonomous (and often geographically separate and/or distant) computer systems.
Scalability: Parallel computing systems are less scalable than distributed computing systems because the memory is shared and limited by the capacity of the main computer. Distributed computing systems can scale more easily, as additional computers can be added to the network.
Memory: In parallel computing, all processors share the same memory and communicate with each other using this shared memory. Distributed computing systems have their own memory and processors, and they communicate through the network.
Synchronization: In parallel computing, processors need to synchronize their operations periodically to ensure that they are working correctly. Distributed computing systems do not require synchronization, as they work independently and communicate through the network.

In summary, parallel computing focuses on using multiple processors within a single computer to speed up tasks, while distributed computing involves multiple independent computers working together on divided tasks. Both approaches have their advantages and are suited to different types of computational tasks.

On this page

What is the Difference Between Parallel and Distributed Computing?

Comparative Table: Parallel vs Distributed Computing

Here is a table comparing parallel and distributed computing:

Feature	Parallel Computing	Distributed Computing
Processors	Multiple processors on a single computer	Multiple autonomous computers
Memory	Shared or distributed memory	Distributed memory
System Type	Single computer with multiple processors	Multiple interconnected computers
Communication	Processors communicate through a bus	Computers communicate through a network
Synchronization	Synchronized with a single master clock	Less synchronization, more flexible
Scalability	Less scalable, limited by the memory capacity of the single computer	More scalable, can easily scale with additional computers
Fault Tolerance	Higher fault tolerance due to multiple processors	Lower fault tolerance, depends on individual computer reliability
Resource Sharing	Improves resource sharing within a single computer	Improves resource sharing across multiple computers
Performance	Concurrency, faster execution of a single task	Improves system performance and resource utilization
Use Cases	Scientific simulations, data processing, graphics rendering	Web applications, data analysis, distributed systems

In parallel computing, multiple processors on a single computer perform multiple tasks simultaneously, with memory being shared or distributed. This approach provides concurrency and saves time and money. Distributed computing, on the other hand, involves multiple autonomous computers that communicate and collaborate over a network to achieve a common goal. Distributed computing improves system scalability, fault tolerance, and resource sharing capabilities.

Guilherme Mazui

Guilherme Mazui is graduated in journalism from the Federal University of Minas Gerais (UFMG) and a master's degree in Communication from the University of São Paulo (USP). In addition, he has experience in advertising writing and has worked as a content editor in several companies.