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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

Understanding blocking, nonblocking, and asynchronous operations

Understanding the different approaches to task execution is extremely important to model and conceive a scalable solution. Knowing when to use asynchronous, blocking, and nonblocking operations can make an enormous difference in the response time of a system.

Understanding blocking operations

In the case of a blocking operation, we can use the example of attending a customer at a bank counter. When the customer's number is called for attendance, all the attention of the cashier is focused on this specific customer. Until the necessity of the current customer is achieved, the cashier can't attend another customer simultaneously. Now, with this in mind, imagine a bank agency with only two cashiers and an influx of 100 customers per hour; we have then a flow problem. This case illustrates the blocking of processing, when a task needs to wait for another to end, blocking the access to resources.

Tip

In the blocking of processing...

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