Multiprocessing with python

Dec 17, 200893 likes54,805 views

This document provides an overview of concurrency in Python using multiprocessing and threading. It begins by introducing the speaker and defining key terms like concurrency, threads, and processes. It then discusses the benefits and use cases of threads versus processes. The document also covers the Global Interpreter Lock (GIL) in Python and how multiprocessing can help avoid it. It provides an example benchmark showing multiprocessing can significantly outperform threading for CPU-bound tasks. Finally, it discusses key aspects of Python's multiprocessing module like Process, Queue, Pool, and Manager classes.

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This document discusses multithreading in Python. It defines multitasking as the ability of an operating system to perform different tasks simultaneously. There are two types of multitasking: process-based and thread-based. A thread is a flow of execution within a process. Multithreading in Python can be achieved by importing the threading module. Multithreading is useful when multiple independent tasks need to be performed. The document outlines three ways to create threads in Python: without creating a class, by extending the Thread class, and without extending the Thread class. The advantages of multithreading include enhanced performance, simplified coding, and better CPU utilization.

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