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Parallel Programming with Python

Parallel Programming with Python

By : Palach
3 (9)
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Parallel Programming with Python

Parallel Programming with Python

3 (9)
By: Palach

Overview of this book

Starting with the basics of parallel programming, you will proceed to learn about how to build parallel algorithms and their implementation. You will then gain the expertise to evaluate problem domains, identify if a particular problem can be parallelized, and how to use the Threading and Multiprocessor modules in Python. The Python Parallel (PP) module, which is another mechanism for parallel programming, is covered in depth to help you optimize the usage of PP. You will also delve into using Celery to perform distributed tasks efficiently and easily. Furthermore, you will learn about asynchronous I/O using the asyncio module. Finally, by the end of this book you will acquire an in-depth understanding about what the Python language has to offer in terms of built-in and external modules for an effective implementation of Parallel Programming. This is a definitive guide that will teach you everything you need to know to develop and maintain high-performance parallel computing systems using the feature-rich Python.
Table of Contents (10 chapters)
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9
Index

Discovering PP

The previous section introduced a low-level mechanism to establish communication among the processes using system calls directly. This was necessary to contextualize the communication between processes in the Linux and Unix environments. Now, we will use a Python module, PP, to establish IPC communication not only among local processes, but also physically distributed throughout a computer network.

The available PP module documentation is not extensive. We can find the documents and FAQs at http://www.parallelpython.com/component/option,com_smf/. The API provides a wide notion of how this tool should be used; it is simple and straightforward.

The most important advantage of using PP is the abstraction that this module provides. Some important features of PP are as follows:

  • Automatic detection of number of processors to improve load balance
  • Many processors allocated can be changed at runtime
  • Load balance at runtime
  • Auto-discovery resources throughout the network

The PP module implements...

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