this post was submitted on 14 Oct 2024
18 points (100.0% liked)
Python
6416 readers
26 users here now
Welcome to the Python community on the programming.dev Lemmy instance!
๐ Events
Past
November 2023
- PyCon Ireland 2023, 11-12th
- PyData Tel Aviv 2023 14th
October 2023
- PyConES Canarias 2023, 6-8th
- DjangoCon US 2023, 16-20th (!django ๐ฌ)
July 2023
- PyDelhi Meetup, 2nd
- PyCon Israel, 4-5th
- DFW Pythoneers, 6th
- Django Girls Abraka, 6-7th
- SciPy 2023 10-16th, Austin
- IndyPy, 11th
- Leipzig Python User Group, 11th
- Austin Python, 12th
- EuroPython 2023, 17-23rd
- Austin Python: Evening of Coding, 18th
- PyHEP.dev 2023 - "Python in HEP" Developer's Workshop, 25th
August 2023
- PyLadies Dublin, 15th
- EuroSciPy 2023, 14-18th
September 2023
- PyData Amsterdam, 14-16th
- PyCon UK, 22nd - 25th
๐ Python project:
- Python
- Documentation
- News & Blog
- Python Planet blog aggregator
๐ Python Community:
- #python IRC for general questions
- #python-dev IRC for CPython developers
- PySlackers Slack channel
- Python Discord server
- Python Weekly newsletters
- Mailing lists
- Forum
โจ Python Ecosystem:
๐ Fediverse
Communities
- #python on Mastodon
- c/django on programming.dev
- c/pythorhead on lemmy.dbzer0.com
Projects
- Pythรถrhead: a Python library for interacting with Lemmy
- Plemmy: a Python package for accessing the Lemmy API
- pylemmy pylemmy enables simple access to Lemmy's API with Python
- mastodon.py, a Python wrapper for the Mastodon API
Feeds
founded 1 year ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
Threads all run on the same core, processes can run on different cores.
Because threads run on the same core, the only time they can improve performance is if there are non-cpu tasks in your code - usually I/O operations. Otherwise the only thing multi threading can provide is the appearance of parallelism (as the cpu jumps back and forth between threads progressing each in small steps).
On the other hand, multiprocessing allows you to run code on different cores, meaning you can take full advantage of all your processing power. However, if youre program has a lot of I/O tasks, you might end up bottlenecked by the I/O and never see any improvements.
For the example you mentioned, it's likely threading would be the best as it's got a little less overhead, easier to program, and you're task is mostly I/O bound. However, if the calculations are relatively quick, it's possible you wouldn't see any improvement as the cpu would still end up waiting for the I/O.