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Parallel Processing and Multiprocessing in Python

A number of Python-related libraries exist for the programming of solutions either employing multiple CPUs or multicore CPUs in a symmetric multiprocessing (SMP) or shared memory environment, or potentially huge numbers of computers in a cluster or grid environment. This page seeks to provide references to the different libraries and solutions available.

Symmetric Multiprocessing

Some libraries, often to preserve some similarity with more familiar concurrency models (such as Python's threading API), employ parallel processing techniques which limit their relevance to SMP-based hardware, mostly due to the usage of process creation functions such as the UNIX fork system call. However, a technique called process migration may permit such libraries to be useful in certain kinds of computational clusters as well, notably single-system image cluster solutions (Kerrighed, OpenSSI, OpenMosix being examples).

Advantages of such approaches include convenient process creation and the ability to share resources. Indeed, the fork system call permits efficient sharing of common read-only data structures on modern UNIX-like operating systems.

Cluster Computing

Unlike SMP architectures and especially in contrast to thread-based concurrency, cluster (and grid) architectures offer high scalability due to the relative absence of shared resources, although this can make the programming paradigms seem somewhat alien to uninitiated developers. In this domain, some overlap with other distributed computing technologies may be observed (see DistributedProgramming for more details).

Cloud Computing

Cloud computing is similar to cluster computing, except the developer's compute resources are owned and managed by a third party, the "cloud provider". By not having to purchase and set up hardware, the developer is able to run massively parallel workloads cheaper and easier.

Grid Computing

Trove classifiers

Topic :: System :: Distributed Computing

Editorial Notes

The above lists should be arranged in ascending alphabetical order - please respect this when adding new frameworks or tools.

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