We're sorry but this page doesn't work properly without JavaScript enabled. Please enable it to continue.
Feedback

Running Python code in parallel and asynchronously

Formal Metadata

Title
Running Python code in parallel and asynchronously
Title of Series
Number of Parts
160
Author
License
CC Attribution - NonCommercial - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
Identifiers
Publisher
Release Date2017
LanguageEnglish

Content Metadata

Subject Area
Genre
Abstract
Running Python code in parallel and asynchronously [EuroPython 2017 - Talk - 2017-07-11 - Anfiteatro 2] [Rimini, Italy] My outline will be: 1) What does it mean to run code in parallel in Python? How does it differ from concurrency? Can they be applied at the some time? 2) GIL and why it complicates parallelism in Python (CPython), but only to some extent. 3) Difference between a thread and a process from the OS point of view. 4) When parallelism in Python is useful and when to avoid it. 5) Description of how to achieve parallel execution in CPython and how to do it properly. 6) Possible traps when using parallel programming in Python. 7) What happens if the code runs both in parallel and asynchronously? 8) Is it really beneficial? 9) How such execution can be achieved? As the outline shows I will focus on the parallel part as it is an important topic in our current time of multicore processors and multiprocessor systems. The topic has been discussed a lot of times but mainly from the scientific point of view, where it's been used for speeding up calulcations time. I will not go into these use cases (e.g. using MPI) but rather discuss it from web development point of view (e.g. multi worker applications)