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What Is Parallel Processing? Definition, Types, and Examples

Essential to contemporary system operations, parallel processing supports multiple streams of data processing tasks through multiple CPUs working concurrently

Parallel processing is a computing technique when multiple streams of calculations or data processing tasks co-occur through numerous central processing units (CPUs) working concurrently This article explains how parallel processing works and examples of its application in real-world use cases

• What Is Parallel Processing?
• How Does Parallel Processing Work?
• Types of Parallel Processing Parallel processing is a computing technique when multiple streams of calculations or data processing tasks co-occur through numerous central processing units (CPUs) working concurrently

Pictorial Representation of Parallel Processing and its Inner Workings Parallel processing uses two or more processors or CPUs simultaneously to handle various components of a single activity Systems can slash a program’s execution time by dividing a task’s many parts among several processors Multi-core processors, frequently found in modern computers, and any system with more than one CPU are capable of performing parallel processing

For improved speed, lower power consumption, and more effective handling of several activities, multi-core processors are integrated circuit (IC) chips with two or more CPUs Most computers can have two to four cores, while others can have up to twelve Complex operations and computations are frequently completed in parallel processing

At the most fundamental level, the way registers are used distinguishes between parallel and serial operations Shift registers operate serially, processing each bit one at a time, whereas registers with parallel loading process each bit of the word simultaneously It is possible to manage parallel processing at a higher level of complexity by using a variety of functional units that perform the same or different activities simultaneously

The interest in parallel computing began in the late 1950s, and developments in supercomputers started to appear in the 1960s and 1970s These multiprocessors used shared memory space and carried out parallel operations on a single data set When the Caltech Concurrent Computation project constructed a supercomputer for scientific applications using 64 Intel 8086/8087 processors in the middle of the 1980s, a new type of parallel computing was introduced

This system demonstrated that one could attain high performance with microprocessors available off the shelf in the general market As the ASCI Red supercomputer computer broke the threshold of one trillion floating point operations per second in 1997, these massively parallel processors (MPPs) emerged to dominate the upper end of computing MPPs have since expanded in number and influence

Clusters entered the market in the late 1980s and replaced MPPs for many applications A cluster is a parallel computer comprised of numerous commercial computers linked together by a commercial network Clusters are the workhorses of scientific computing today and dominate the data centers that drive the modern information era Based on multi-core processors, parallel computing is becoming increasingly popular

Parallel processing makes it possible to use regular desktop and laptop computers for solving problems that used to require a powerful supercomputer and the help of expert network and data center managers Until the middle of the 1990s, computers made for consumers could only process data one at a time Most operating systems today control how different processors work together This makes parallel processing more cost-effective than serial processing in most cases

Parallel computing is becoming critical as more Internet of Things (IoT) sensors, and endpoints need real-time data Given how easy it is to get processors and GPUs (graphics processing units) today through cloud services, parallel processing is a vital part of any microservice rollout

See More: What Is Ailing IoT Implementations at Scale and Ways to Fix Them In general, parallel processing refers to dividing a task between at least two microprocessors The idea is very straightforward: a computer scientist uses specialized software created for the task to break down a complex problem into its component elements Then, they designate a specific processor for each part To complete the entire computing problem, each processor completes its portion The software reassembles the data to solve the complex initial challenge

When processing is done in parallel, a big job is broken down into several smaller jobs better suited to the number, size, and type of available processing units After the task is divided, each processor starts working on its part without talking to the others Instead, they use software to stay in touch with each other and find out how their tasks are going