Parallel Processing involves dividing a computational task into smaller subtasks that can be processed simultaneously by multiple processors. This approach enhances performance and efficiency, enabling faster processing of large datasets and complex calculations commonly used in scientific computing, data analysis, and real-time applications.
What are the benefits of Parallel Processing?
The benefits of Parallel Processing include improved performance, faster processing times for large datasets, and the ability to handle complex computations more efficiently by leveraging multiple processors simultaneously.
How does Parallel Processing differ from sequential processing?
Parallel Processing differs from sequential processing in that tasks are divided into smaller subtasks that are processed simultaneously by multiple processors, while sequential processing handles tasks one at a time in a single sequence.
What are common applications of Parallel Processing?
Common applications of Parallel Processing include scientific simulations, data analysis, real-time systems, and rendering graphics in computer games, where large amounts of data need to be processed quickly and efficiently.