Distributed and Parallel Computing with/for Python.

## Project description

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).

dispy works with Python versions 2.7+ and 3.1+ on Linux, Mac OS X and Windows; it may work on other platforms (e.g., FreeBSD and other BSD variants) too.

## Features

• dispy is implemented with pycos, an independent framework for asynchronous, concurrent, distributed, network programming with tasks (without threads). pycos 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. pycos 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, pycos can be used to create distributed communicating processes, for broad range of use cases.

• 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.

## Dependencies

dispy requires pycos for concurrent, asynchronous network programming with tasks. pycos is automatically installed if dispy is installed with pip. Under Windows efficient polling notifier I/O Completion Ports (IOCP) is supported only if pywin32 is installed; otherwise, inefficient select notifier is used.

## Installation

To install dispy, run:

python -m pip install dispy

## Release Notes

Short summary of changes for each release can be found at News. Detailed logs / changes are at github commits.

## Authors

• Giridhar Pemmasani

## Project details

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