Parallel Versus Distributed Computing

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It’s likely that if you’ve ever thought about working in the field of computer science, you’ve heard a number of phrases used without really understanding what they meant.

Parallel computing and distributed computing are two of these phrases that are commonly used. Despite the fact that they both pertain to the same broad subject matter, they each involve unique procedures and applications that should be taken into account.

The definitions of these concepts, their distinctions and similarities, and how they apply to the quickly changing field of computer science are all covered below.

What is parallel computing?

Parallel computing involves multiple devices carrying out the tasks assigned to them at precisely the same moment. In this case, the processors are connected to each other via a single computer and have the role of breaking down and completing the tasks. The tasks are split up into smaller steps known as subtasks, allowing the multiple processors to complete them more efficiently and accurately.

There are three types of parallel computing that you will hear about when working in computer science:

  • Task-level parallelism
  • Bit-level parallelism
  • Instruction-level parallelism

Although each of these is a little bit different in terms of how it operates, they still function following the same basic process using multiple processors and one computer.

What is distributed computing?

Parallel and distributed computing operate in many ways that are quite similar. When you dissect it, though, you see that they are nearly the complete opposite.

Distributed computing makes use of numerous computers as opposed to only one to more precisely complete high-speed calculations. This enables the operators to build a kind of enormous supercomputer that is capable of performing calculations faster than any single computer could.

The second significant distinction between the two is that this supercomputer will carry out only one job using the combined power of all the computers, as opposed to carrying out many tasks. This leads to far faster turnaround times and more precise findings.

Primary differences

Although they were both originally designed to do practically the same thing, there are some key differences between the two that heavily influence their overall functionality.


The synchronization of the systems is one of the most noticeable contrasts between these robust systems.

Since every CPU used in parallel computing is connected to the same machine and is thus operating at the same time, real synchronization is achievable. Distributed systems, on the other hand, employ an algorithm to attempt to attain the same outcome. It can be challenging to reach the same degree of accuracy just by employing an algorithm with several linked computers.


The other huge difference between the systems, which sets them apart in terms of usage, is scalability. There is virtually no limit to how big a distributed system can be, you just need to have a big enough room to accommodate all the computers and enough cables to connect them while maintaining adequate ventilation.

On the other hand, you can only install so many processors in a single computer before you overload its capacity. This means that if you’re trying to build a multi-room supercomputer, you will find it difficult to do parallel computing.

How are they used?

The initial query that most individuals have is how these two processes are employed after learning what they signify. After all, the majority of computers only have one CPU, and they appear to be capable of doing almost any task. Once you enter the field of computer science though, this isn’t the case.

You will discover how these systems can relate to advanced algorithms, software engineering, databases, data communications and applied artificial intelligence.

Which is better?

When comparing the two forms of computing, distributed computing is typically seen as being superior for one key reason. With distributed computing, the size of the supercomputer is essentially unlimited. You can just keep connecting computers until you reach the required power, and each computer can have multiple CPUs.

There is somewhat of a limit to how far parallel computing can go because it only employs one machine and several processors. There is a limit to the number of processors that can be connected to each computer, even though each one has a varied capability. While this number may be quite large, it will never be able to match distributed computing’s virtually infinite ability.

In terms of computer science applications where entire rooms are dedicated to the computer system, distributed computing allows for a much more powerful system to be created. However, in terms of accessibility and pricing, parallel computing is much more accessible for smaller companies and individuals who still want access to a powerful computing system.

Whichever job in computer science you pursue, both these systems may be explored, and computer scientists need the knowledge and skillset to navigate them. At Baylor University, their Online Masters in Computer Science offers a selection of specializations, allowing them to focus on the specific areas of computer science that align with their interests and career goals. Students will have the opportunity to learn about advanced algorithms, software engineering, databases, data communications and applied artificial intelligence and their relation to parallel and distributed computing systems.


While both parallel and distributed computing aim to build a powerful computing system, they go about it in essentially different ways. The first employs multiple connected computers, each with a separate CPU, whereas the second employs a single computer with multiple processors.

Despite taking distinct approaches, both succeed in delivering a system that can carry out activities at a rate that was previously seen as impractical. These systems play a significant role in making professions in computer science so fascinating. You are continuously resolving issues and looking for fresh approaches to optimize computer performance.