• Concurrency

Concurrency

Central clearing house for Concurency related issues and discussion

Links

• Concurrency SIG Mailing List

• Concurrency/99Bottles - solutions to common problems in different styles/toolkits

• Concurrency/Patterns - ways to structure your concurrent program

• Concurrency/TipsAndTricks - Hints for writing better concurrent code

• Beware the GlobalInterpreterLock

  • Note that CPython 2.x and 3.[01] suffer from a GIL, as does PyPy 1.4.1

  • CPython 3.2 still has a GIL, but it's purportedly less troublesome

  • Jython and IronPython have no GIL, nor any GIL-related concurrency issues

  • Cython has the ability to selectively release the GIL using a "with" statement

Standard Library

• multiprocessing (available in CPython 2.6 and up, also in recent PyPy 1.5 alpha builds)

• threading - make your GIL-burdened life easy and your code fast: use multiprocessing.

• CPython 3.2 (and up): concurrent.futures

Frameworks

See also: ParallelProcessing, Pypi Distributed Computing Trove

• StacklessPython - A Python Implementation That Does Not Use The C Stack. It purportedly can handle thousands of threads (tasklets) in the context of a very parallel game application, but remains pretty single-core.

• asyncoro - Framework for asynchronous, concurrent, distributed and network programming.

• Fibra - Fibra provides advanced cooperatve concurrency using Python generators.

• Cogen - cogen is a crossplatform library for network oriented, coroutine based programming using the enhanced generators. The project aims to provide a simple straightforward programming model similar to threads but without all the problems and costs.

• Greenlet - The "greenlet" package is a spin-off of StacklessPython, a version of CPython that supports micro-threads called "tasklets". Tasklets run pseudo-concurrently (typically in a single or a few OS-level threads) and are synchronized with data exchanges on "channels". Documentation

• Gevent - coroutine-based networking library that uses greenlet to provide a high-level synchronous API on top of libevent event loop. It features standard library-inspired API and high performance.

• Eventlet - Eventlet is a networking library written in Python. It achieves high scalability by using non-blocking io while at the same time retaining high programmer usability by using coroutines to make the non-blocking io operations appear blocking at the source code level.

• Circuits - circuits is a Lightweight, Event driven Framework with a strong Component Architecture.

• Twisted - Full-featured and well-tested asynchronous networking library. Concurrency model: Asynchronous code written around central objects known as Deferreds. Cooperative task scheduler via the Cooperator object. Time based scheduling with LoopingCall and reactor.callLater.

• Kamaelia - In Kamaelia you build systems from simple components that talk to each other. This speeds development, massively aids maintenance and also means you build naturally concurrent software. It's intended to be accessible by any developer, including novices. It also makes it fun.

• Concurrence - Concurrence is a framework for creating massively concurrent network applications in Python. It takes a Lightweight-tasks-with-message-passing approach to concurrency.

• Parallel Python - Parallel Python is a python module which provides mechanism for parallel execution of python code on SMP (systems with multiple processors or cores) and clusters (computers connected via network).

• pprocess - The pprocess module provides elementary support for parallel programming in Python using a fork-based process creation model in conjunction with a channel-based communications model implemented using socketpair and poll.

• pysage - lightweight high-level message passing library supporting actor based concurrency

• pypes - Pypes is a framework for designing highly scalable concurrent Flow-Based applications. It uses a component model (Stackless tasklets) to achieve modularity (very loose coupling) along with the 2.6 multiprocessing module to achieve parallelism (multi-processing support). The goals are focused on performance (capable of processing millions of documents) and ease of use. Building custom components is simple and pypes offers Paste templates for generating boiler plate component code along with supporting build files (plugins/components are eggs). Pypes also provides a full Web 2.0 WSGI user interface that allows users to create, destroy, configure, and connect components together using simple drag-n-drop functionality from any standards based web browser.

• diesel - Diesel is a framework for writing network applications using asynchronous I/O in Python. It uses Python's generators to provide a friendly syntax for coroutines and continuations. It performs well and handles high concurrency with ease.

• Chiral - Chiral is a lightweight coroutine-based networking framework for high-performance internet and Web services.

Presentations etc.

• Using Coroutines to Create Efficient, High-Concurrency Web Applications - Matt Spitz, Pycon 2011 Video

• Concurrency and Distributed Systems - Jesse Noller, Pycon 2009 Slides Video

• Introduction to Multiprocessing - Jesse Noller, Pycon 2009 Slides Video

• Inside the GIL - a detailed analysis of how the GIL is even worse than we thought by David Beazley, ChiPy June 2009 Slides Video