Multiprocessing vs multithreading vs asyncio

python

concurrency-model

asyncio

multiprocessing

byAlex Kataev·Mar 1, 2025

Here's a quick guide to picking the right tool in Python:

Multiprocessing: Used for CPU-bound tasks. It utilizes multiple CPUs to sidestep the GIL limitation.

from multiprocessing import Process

def compute_heavy(): pass  # "Heavy computation" aka finding a needle in N haystacks
Process(target=compute_heavy).start()

Multithreading: Ideal for jobs waiting on I/O operations. Threads can overlap their waiting periods, countering the GIL, thanks to I/O release.

from threading import Thread

def disk_io_bound(): pass  # Waiting for I/O is like hoping for rain in the desert
Thread(target=disk_io_bound).start()

Asyncio: Best for I/O-bound, asynchronous operations. Tasks can switch efficiently during wait periods without threading complexity.

import asyncio

async def async_io_operation(): pass  # I/O is so slow; you might finish War and Peace!
asyncio.run(async_io_operation())

In a nutshell, multiprocessing for CPU tasks, multithreading for synchronously waiting tasks, and asyncio for async I/O tasks.

Commanding the right concurrency model

Just like selecting the right tool for a DIY project, the right concurrency model is essential for efficient code execution and application maintainability.

Threading: Fast I/O, Limited Connections

Multithreading excels with I/O-bound tasks with fast I/O and limited connections.
It's especially efficient for external communication-heavy applications where minimal CPU usage is needed.

Asyncio and uvloop: Slow I/O, Many Connections

When dealing with slow I/O and numerous connections, such as long-polling or websockets, asyncio's non-blocking approach optimizes request handling.
For boosting asyncio performance, use uvloop which can make it 2-4x faster. A great example is Japranto, an HTTP server utilizing uvloop for fast pipelining.

Multiprocessing: CPU Bound tasks

For CPU-intensive tasks, multiprocessing distributes the load across multiple cores, resulting in decreased run times.
Note: Interprocess communication often introduces overhead and not all tasks can be effectively parallelized - choose carefully for maximum benefits.

Simplified concurrency with concurrent.futures

For simpler concurrency requirements, use concurrent.futures.Executor which offers a simplified API for threading and multiprocessing alike.

Granular control with asyncio

Achieve exact control over context switching with asyncio's async and await. To simplify calls to traditional synchronous functions, use asyncio.to_thread in Python 3.9.

Top-notch performance and scalability

Choosing the right concurrency model is just step one. Here are some pro tips to optimize and scale your code:

Asyncio and uvloop: Performance Gains

Use asyncio with uvloop for performance on par with NodeJS and Go.
Keep an eye out for blocking calls which can nullify asyncio's benefits. Stick to asynchronous libraries.

Threading & multiprocessing: Watch out for...

Always use thread-safe structures or primitives to avoid issues in multithreading.
Remember: improper multiprocessing management can lead to excessive CPU context switches and overhead.

Common pitfalls to avoid

Not all tasks can be efficiently broken down into parallelizable chunks. Some algorithms are inherently sequential.
Curing all performance issues with threading or multiprocessing is a myth. Concurrency isn't always the answer.

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