MultiProcessing in Python _ Python Tutorial - Day #98

In the video "MultiProcessing in Python - Python Tutorial - Day #98", viewers explore multiprocessing, a technique for achieving parallelism by executing multiple processes simultaneously. The tutorial covers the basics of multiprocessing in Python, introducing the multiprocessing module for creating and managing processes. Viewers learn how to define and start processes, share data between processes using shared memory or communication channels like queues and pipes, and synchronize access to shared resources using locks and semaphores. Practical examples demonstrate how to use multiprocessing to improve the performance of CPU-bound tasks, such as data processing, computation-heavy operations, and simulations, by distributing the workload across multiple CPU cores. By mastering multiprocessing, viewers can develop Python applications that take advantage of parallel processing to speed up execution and maximize system resources utilization. This tutorial is essential for Python developers seeking to leverage parallelism and concurrency in their applications and optimize performance for computationally intensive tasks.