How deeply does Python Concurrency with asyncio cover multiprocessing module?

Builds direct Process examples through target functions, arguments, start, join, and timing before deferring pools and shared-state coordination.

Multiprocessing module

As explained in Python Concurrency with asyncio

The multiprocessing module is Python’s standard-library module for creating and managing processes.

The multiprocessing module creates and manages child processes for concurrent and parallel execution. Each process has its own Python interpreter and global interpreter lock, allowing CPU-bound work to use multiple CPU cores effectively. section 6.0 section 6.1

Create a Process with a target function and argument tuple, call start to begin it, then call join so the parent waits for completion. Unlike threads, processes avoid having one interpreter’s GIL prevent parallel Python bytecode execution. section 6.1 section 1.4.2

Running two CPU-bound functions

A counting function runs in two child processes, one counting to 100,000,000 and the other to 200,000,000. Starting both before joining lets their work overlap; the parent then waits for both and reports total elapsed time.

How Python Concurrency with asyncio covers it

Cover of Python Concurrency with asyncio

liveBook manning.com

Read Python Concurrency with asyncio on liveBook Get the book at manning.com

Related concepts

asyncioused by · asyncio is Python’s standard-library framework for asynchronous programming, built around coroutines, awaitable objects, and event loops.
multiprocessingimplements · Multiprocessing is a programming technique that executes work in multiple operating-system processes, enabling concurrent or parallel execution across available processor resources.
processuses · Process is the asyncio subprocess interface representing a managed child process and exposing its lifecycle and standard-stream operations.
global interpreter lockcontrasts with · The global interpreter lock is a mutual-exclusion mechanism in CPython that permits only one thread at a time to execute Python bytecode within a process.
compute-boundaddresses · A computation is compute-bound when its performance is limited primarily by available processor or arithmetic capacity rather than memory transfer, input/output, or other resources.

This explainer was generated by Manning's knowledge graph from the text of Python Concurrency with asyncio. The section links point to the parts of the book it draws on.