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Python High Performance, Second Edition

You're reading from   Python High Performance, Second Edition Build high-performing, concurrent, and distributed applications

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Product type Paperback
Published in May 2017
Publisher Packt
ISBN-13 9781787282896
Length 270 pages
Edition 2nd Edition
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Author (1):
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Dr. Gabriele Lanaro Dr. Gabriele Lanaro
Author Profile Icon Dr. Gabriele Lanaro
Dr. Gabriele Lanaro
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Table of Contents (10) Chapters Close

Preface Benchmarking and Profiling FREE CHAPTER Pure Python Optimizations Fast Array Operations with NumPy and Pandas C Performance with Cython Exploring Compilers Implementing Concurrency Parallel Processing Distributed Processing Designing for High Performance

Using multiple processes

The standard multiprocessing module can be used to quickly parallelize simple tasks by spawning several processes, while avoiding the GIL problem. Its interface is easy to use and includes several utilities to handle task submission and synchronization.

The Process and Pool classes

You can create a process that runs independently by subclassing multiprocessing.Process. You can extend the __init__ method to initialize resources, and you can write the portion of the code that will be executed in a subprocess by implementing the Process.run method. In the following code, we define a Process class that will wait for one second and print its assigned id:

    import multiprocessing 
import time

class Process(multiprocessing.Process):
...
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