What is parallel processing?

The term "parallel processing" describes the practice of running numerous processes in parallel on different computers. 

 

A number of processes, or activities, working in tandem to resolve an issue is what makes up a parallel program. Using a divide-and-conquer strategy, a given task is split into several subtasks, and each subtask is executed on a separate central processing unit (CPU). 

 

In a multi-processor system, parallel programming refers to the use of the divide-and-conquer technique. More processing power is needed by many modern applications than what a conventional sequential computer can provide. Through the use of an efficient communication system and an increase in the number of central processing units (CPUs) in a computer, parallel processing offers a cost-effective solution to this issue. This allows for the task to be distributed among multiple CPUs. 

 

In comparison to a single-processor system, this configuration provides superior computing power and performance. A great deal of stuff is influencing the evolution of parallel processing. Some of the most notable ones are: 

• The scientific and commercial computer communities are driving up the computational requirements. Aerospace, mechanical design and analysis, geographic information systems, biological sciences, and similar fields are the domains of the technically challenging computing problems that necessitate rapid processing capacity. 
• Due to physical constraints imposed by the laws of thermodynamics and the speed of light, sequential designs are approaching their physical limits. 
• Connecting numerous CPUs provides an alternate means of achieving high computing performance, while the operating speed of sequential CPUs is approaching its limit (no more vertical development). 
• Advancements in hardware such as superscalar, pipelining, and similar methods necessitate advanced compiler technology and are not scalable. It is not an easy effort to develop such compiler technology. 
• Vector processing is effective for specific types of issues. It excels at scientific tasks that require extensive matrix operations as well as graphics processing. 
• Databases and other fields cannot benefit from it. There has been a lot of research and development on development tools and environments, and the technology behind parallel processing is now ready to be used commercially. 
• The groundwork for heterogeneous computing has been laid by recent advances in networking technologies.

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