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1

dispy

Distributed and Parallel Computing with/for Python.

dispy is a generic and comprehensive, yet easy to use framework for creating and using compute clusters to execute computations in parallel across multiple processors in a single machine (SMP), among many machines in a cluster, grid or cloud. dispy is well suited for data parallel (SIMD) paradigm where a computation (Python function or standalone program) is evaluated with different (large) datasets independently. dispy supports public / private / hybrid cloud computing, fog / edge computing.

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Downloads: 49 This Week

Last Update: 2022-10-06
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2

asyncoro

Python framework for asynchronous, concurrent, distributed programming

asyncoro is a Python framework for developing concurrent, distributed, network programs with asynchronous completions and coroutines. Asynchronous completions implemented in asyncoro are sockets (non-blocking sockets), database cursors, sleep timers and locking primitives. Programs developed with asyncoro have same logic and structure as Python programs with threads, except for a few syntactic changes. asyncoro supports socket I/O notification mechanisms epoll, kqueue, /dev/poll (and poll...

Downloads: 0 This Week

Last Update: 2017-05-23
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3

pipper

Python Integrated Parallel Programming EnviRonment (PIPPER), Python pre-parser that is designed to manage a pipeline, written in Python. It enables automated parallelization of loops. Think of it like OpenMP for Python, but it works in a computer cluster

Downloads: 0 This Week

Last Update: 2013-05-16
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4

MPY (MPi for pYthon)

MPY is an MPI implementation for Python using MPICH (or any other MPI implementation). MPY also provides helper functions for ease of programming, and a simple plug-in interface.

Downloads: 0 This Week

Last Update: 2013-03-07
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