Parallelism, Concurrency, and AsyncIO in Python

Parallelism, Concurrency, and AsyncIO in Python

In this post looks at how to speed up CPU-bound and IO-bound operations with multiprocessing, threading, and AsyncIO.

This post looks at how to speed up CPU-bound and IO-bound operations with multiprocessing, threading, and AsyncIO.

Concurrency vs Parallelism

Concurrency and parallelism are similar terms, but they are not the same thing.

Concurrency is the ability to run multiple tasks on the CPU at the same time. Tasks can start, run, and complete in overlapping time periods. In the case of a single CPU, multiple tasks are run with the help of  context switching, where the state of a process is stored so that it can be called and executed later.

Parallelism, meanwhile, is the ability to run multiple tasks at the same time across multiple CPU cores.

Though they can increase the speed of your application, concurrency and parallelism should not be used everywhere. The use case depends on whether the task is CPU-bound or IO-bound.

Tasks that are limited by the CPU are CPU-bound. For example, mathematical computations are CPU-bound since computational power increases as the number of computer processors increases. Parallelism is for CPU-bound tasks. In theory, If a task is divided into n-subtasks, each of these n-tasks can run in parallel to effectively reduce the time to 1/n of the original non-parallel task. Concurrency is preferred for IO-bound tasks, as you can do something else while the IO resources are being fetched.

python parallelism concurrency asyncio

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