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dispy 4.5.2

Distributed and Parallel Computing with/for Python.

dispy is a 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 is evaluated with different (large) datasets independently with no communication among computation tasks (except for computation tasks sending intermediate results to the client). If communication/cooperation among tasks is needed, asyncoro framework could be used.

dispy works with Python versions 2.7+ and 3.1+. It has been tested with Linux, OS X and Windows; it may work on other platforms too.


  • dispy is implemented with asyncoro, an independent framework for asynchronous, concurrent, distributed, network programming with coroutines (without threads). asyncoro uses non-blocking sockets with I/O notification mechanisms epoll, kqueue and poll, and Windows I/O Completion Ports (IOCP) for high performance and scalability, so dispy works efficiently with a single node or large cluster(s) of nodes. asyncoro itself has support for distributed/parallel computing, including transferring computations, files etc., and message passing (for communicating with client and other computation tasks). While dispy can be used to schedule jobs of a computation to get the results, asyncoro can be used to create distributed communicating processes (even with different computations).

  • Computations (Python functions or standalone programs) and their dependencies (files, Python functions, classes, modules) are distributed automatically.

  • Computation nodes can be anywhere on the network (local or remote). For security, either simple hash based authentication or SSL encryption can be used.

  • After each execution is finished, the results of execution, output, errors and exception trace are made available for further processing.

  • Nodes may become available dynamically: dispy will schedule jobs whenever a node is available and computations can use that node.

  • If callback function is provided, dispy executes that function when a job is finished; this can be used for processing job results as they become available.

  • Client-side and server-side fault recovery are supported:

    If user program (client) terminates unexpectedly (e.g., due to uncaught exception), the nodes continue to execute scheduled jobs. If client-side fault recover option is used when creating a cluster, the results of the scheduled (but unfinished at the time of crash) jobs for that cluster can be retrieved later.

    If a computation is marked reentrant when a cluster is created and a node (server) executing jobs for that computation fails, dispy automatically resubmits those jobs to other available nodes.

  • dispy can be used in a single process to use all the nodes exclusively (with JobCluster - simpler to use) or in multiple processes simultaneously sharing the nodes (with SharedJobCluster and dispyscheduler program).

  • Cluster can be monitored and managed with web browser.


dispy requires asyncoro for concurrent, asynchronous network programming with coroutines. asyncoro can be installed for Python 2.7+ with:

pip install asyncoro

or for Python 3.1+ with:

pip3 install asyncoro

Under Windows efficient polling notifier I/O Completion Ports (IOCP) is supported only if pywin32 is installed; otherwise, inefficient select notifier is used.


To install dispy for Python 2.7+, run:

pip install dispy

or to install dispy for Python 3.1+, run:

pip3 install dispy


  • Giridhar Pemmasani
File Type Py Version Uploaded on Size
dispy-4.5.2.tar.gz (md5) Source 2015-07-23 281KB
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