Parallel Processing
Parallel processing is a computing technique that divides a large task into smaller sub-tasks that can be executed simultaneously across multiple processors or cores to improve performance and efficiency. It leverages concurrency to reduce overall execution time, particularly for computationally intensive or data-heavy operations. This concept is fundamental in high-performance computing, big data analytics, and modern multi-core systems.
Developers should learn parallel processing to optimize applications that handle large datasets, complex simulations, or real-time processing, such as in scientific computing, machine learning training, or video rendering. It is essential for leveraging modern multi-core CPUs and GPU architectures to achieve scalability and reduce latency in performance-critical systems. Understanding parallel processing helps in designing efficient algorithms and avoiding bottlenecks in distributed or concurrent environments.