How does the parallel processing system work?

How does a parallel processing system work?

Parallel processing is an important method for improving the performance of computing systems, which is very effective for performing complex and large tasks. This method uses multiple processors or cores to perform tasks simultaneously and significantly reduces their execution time. By dividing the work between processors, the execution time of tasks is significantly reduced and system performance is improved. This method is effective for tasks that are time-consuming due to the complexity and high volume of data when using a regular processor. In addition, increasing the number of processors or cores can be easily done, which helps the system’s scalability. Since tasks are performed concurrently, this method also improves system efficiency.  

How a parallel processing system works

A parallel processing system works by dividing a complex task into several parts, with each part assigned to a processor. Then, each processor solves its respective part in parallel. Each processor works simultaneously, performing operations according to instructions and retrieving data from system memory. Also, specialized software is used to establish communication between processors so that it can synchronize changes in data. If all processors are coordinated with each other, at the end of the operation, the software completely collects and combines all data components.  

Types of Parallel Processing

• SIMD (Single Instruction, Multiple Data): In this type of parallel processing system, multiple processors execute the same instructions and operate on different data. This method is commonly used for processing large datasets that can be analyzed to a common pattern.  
• MIMD (Multiple Instruction, Multiple Data): In MIMD, each processor has independent instructions and data. This type of parallel processing is suitable for situations where the instructions and data are diverse and independent of each other. Each processor can receive and process data from different data streams.  
• MISD (Multiple Instruction, Single Data): In this case, each processor uses different algorithms or instructions for one input dataset. This type of parallel processing is less common and is used in specific cases.

Conclusion

Throughout history, the development of parallel processing systems has gone through various stages. In this path, different types of parallel processing were introduced, including SIMD, MIMD, and MISD, each of which has its own applications, advantages, and disadvantages. With the advancement of technology and the growth of computing needs, it is expected that parallel processing, as one of the main solutions for improving the efficiency and performance of computing systems, will continue to develop in the future and play an important role.  

In summary, parallel processing enhances computational speed and efficiency by dividing tasks among multiple processors that operate simultaneously. Different architectures like SIMD, MIMD, and MISD cater to various types of data and processing needs.