Concurrency and Parallelism in Python

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This document examines concurrency and parallelism as fundamental techniques for optimizing Python applications. It distinguishes between concurrency for I/O-bound tasks using threading and asyncio, parallelism for CPU-bound tasks using multiprocessing, and strategies for combining both approaches to create high-performance applications that efficiently manage both computational workloads and external resource dependencies.

Understanding Concurrency and Parallelism

Defining Concurrency

In Python, concurrency can be used to allow multiple tasks to make progress at the same time, even if they don’t actually run simultaneously. This is useful when optimizing how tasks are scheduled and resources are used, especially for I/O-bound tasks.

Parallelism on Ghafoor's Personal Blog

Concurrency and Parallelism in Python

Understanding Concurrency and Parallelism

Defining Concurrency