Parallel Processing

What is Parallel Processing?

Parallel processing refers to the technique of distributing the computer processes evenly between two or more processors or cores of the CPU.

In this technique, a single activity is divided into several smaller parts and assigned to different processors or cores in the computer to work concurrently, while being in sync with the help of software.

Understanding Parallel Processing

Understanding Parallel Processing

Parallel processing in computers is a specific technique of computing where several streams of data processing or calculation tasks occur in parallel.

There are quite a few CPUs or Central Processing Units working at the same time to make this happen.

However, this indirectly means that there are two specific requirements in the first place for parallel processing to happen. These are:

At the most basic level of parallel processing, the registers are used in a much better way in this technique than in sequential processing. It processes each bit simultaneously.

When an array of functional components performs simultaneously for the same or different related activities, it helps in removing the higher level of complexities in data processing.

This specific process of data processing can be used to solve problems using a regular desktop or laptop computer that would have needed a supercomputer otherwise.

Also, the operating systems of today determine the number of processors that may work together. This makes parallel processing even more cost-effective as compared to serial processing in most cases.

The principal purpose of parallel processing is to:

Simultaneous processing helps the expert network and data center managers and is integral to process and supply more real-time data to micro service rollout, Internet of Things or IoT sensors, Graphics Processing Units, cloud services, and different endpoints.

In parallel processing, each of the processor or core performs operations in parallel as instructed in the following ways:

When the processing is complete, the software will fit all the data from the different parts of the task together to produce the result of the single task.

What are the Characteristics of Parallel Processing?

Parallel processing can be characterized as either fine-grained or coarse-grained. In fine-grained parallelism, the tasks communicate with each other several times in a second to produce the results in real time, or close to it.

The other is a bit slower process due to the infrequent communication with the tasks.

Basically, it splits a complete task into two or more synchronized task segments and works on them simultaneously using multiple cores or processors.

Each processor performs concurrently, and the nodes share disks, data, and other resources.

Parallel processing can also be characterized by its processing architecture, which can be as follows:

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Ideally, all the processors in parallel processing are tightly coupled, run on the same operating system and they share the same user terminal and secondary storage.

Types of Parallel Processing

Parallel processing can be categorized into several other types based on the method of processing the instructions.

Different types of parallel processing include MPP, SIMD, MISD, SISD, SPMD, and MIMD. Each of these has different features and works differently, which is why some are more popular than the others.

SIMD or Single Instruction, Multiple Data:

MIMD or Multiple Instruction, Multiple Data:

MISD or Multiple Instruction, Single Data:

SISD or Single Instruction, Single Data:

SPMD or Single Program, Multiple Data:

MPP or Massively Parallel Processing:

Parallel Processing Examples

Examples of parallel processing include computer simulations made by the supercomputers used in astronomy and astrophysics, geoprocessing, agriculture, banking and cryptocurrencies, medical imaging, video-post production and others.

Some other examples of parallel processing include several computers, super and otherwise, used for studying, analyzing, or developing:

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Parallel processing also plays a significant role in developing machine learning algorithms and implementing them in several AI programs.

This is because it allows faster processing of more data points and produces more precise and useful insights.

What is Parallel Processing in Operating System

Typically, the operating system used in parallel processing is the same for all the processors. Since these are packed tightly in one casing, they can run on the same OS.

However, the operating system used in the process in particular can be of different types. It can be a separate, symmetric or master/slave operating system.

Separate operating system:

In this type of arrangement, each of the processors will have a separate copy of the operating system along with the file system, memory and I/O devices.

This is typical for a hypercube architecture where every node is like a separate computer. However, this type of parallel processing needs a lot of memory and execution time.

Symmetric operating system:

In this type of environment, every processor of the system is the same in every possible aspect. All data structures of the operating system are located in the global memory, which is accessible by every processor.

There is a microkernel in each processor that helps them work in parallel and when one or two of them fails, it may slow down the system but prevent it from coming to a standstill.

However, it is quite complex and difficult to build or debug.

Master/Slave operating system:

In this arrangement, there is one master processor. This executes the operating system. The other processors are considered slave processors and are used as resources such as memory or I/O devices.

The master processor knows the capabilities of each slave processor and accordingly assigns them tasks and resources.

This means that the master processor is in control of everything, and if it fails, the entire system comes to a halt.

Ideally, an operating system running on a multicore processor can be considered a parallel operating system, such as Windows 7, 8, 10, all of which do parallel processing. In short, most modern operating systems support parallel processing.

What are the Key Elements of Parallel Processing?

The key elements of parallel processing are speedup and scaleup, synchronization, locking and messaging.

Speedup and Scaleup:

These elements help in measuring the goals of parallel processing in the following ways:


This is an important success factor that signifies proper management of concurrent tasks to ensure correctness. The less synchronization needed, the better will be the speedup and scale-up.

Dividing a task needs little synchronization and its amount depends on the number of users, amount of available resources and type of tasks sharing them.

Little synchronization is required for a smaller number of tasks but a lot of it is needed for coordinating a large number of concurrent tasks.


This is one way of synchronizing the tasks. Several different locking mechanisms are required for synchronizing tasks in parallel processing and one such internal locking facility is the Integrated Distributed Lock Manager or IDLM.

This helps in:


And finally, parallel processing needs to communicate between the nodes in operation and therefore it needs a fast and efficient messaging system with very low latency and high bandwidth. This is facilitated by the IDLM.

Does Google Use Parallel Processing?

Yes, Google uses parallel processing which helps it run a distributed network involving thousands of low-cost computers. The technology speeds up the data processing process significantly.

Ideally, there are three discrete parts in Google. These are:

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Parallel processing helps these three parts to perform their functions most precisely in the following ways:

All these actions are performed in just a fraction of a nanosecond, thanks to parallel processing.



Parallel Processing Vs Sequential Processing

Which Generation Uses Parallel Processing?

Typically, parallel processing is a feature of the 5th generation computers and major electronic components that are based on multicore processors and Artificial Intelligence or using ULSI or Ultra Large Scale Integration technology.


Parallel processing is very useful to expedite data processing and has been around for a while.

It has varied applications, being able to process large amounts of data in a quick time.

It helps in financial risk management, big data analysis, medical imaging, video color correction, just to name a few of its uses.