Book Image

Python Parallel Programming Cookbook

By : Giancarlo Zaccone
Book Image

Python Parallel Programming Cookbook

By: Giancarlo Zaccone

Overview of this book

This book will teach you parallel programming techniques using examples in Python and will help you explore the many ways in which you can write code that allows more than one process to happen at once. Starting with introducing you to the world of parallel computing, it moves on to cover the fundamentals in Python. This is followed by exploring the thread-based parallelism model using the Python threading module by synchronizing threads and using locks, mutex, semaphores queues, GIL, and the thread pool. Next you will be taught about process-based parallelism where you will synchronize processes using message passing along with learning about the performance of MPI Python Modules. You will then go on to learn the asynchronous parallel programming model using the Python asyncio module along with handling exceptions. Moving on, you will discover distributed computing with Python, and learn how to install a broker, use Celery Python Module, and create a worker. You will understand anche Pycsp, the Scoop framework, and disk modules in Python. Further on, you will learnGPU programming withPython using the PyCUDA module along with evaluating performance limitations.
Table of Contents (13 chapters)
Python Parallel Programming Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

How to name a process


In the previous example, we identified the processes and how to pass a variable to the target function. However, it is very useful to associate a name to the processes as debugging an application requires the processes to be well marked and identifiable.

How to do it...

The procedure to name a process is similar to that described for the threading library (see the recipe How to determine the current thread in Chapter 2, Thread-based Parallelism, of the present book.)

In the main program, we create a process with a name and a process without a name. Here, the common target is the foo()function:

#Naming a Process: Chapter 3: Process Based Parallelism
import multiprocessing
import time

def foo():
    name = multiprocessing.current_process().name
    print ("Starting %s \n" %name)
    time.sleep(3)
    print ("Exiting %s \n" %name)

if __name__ == '__main__':
    process_with_name = \
                      multiprocessing.Process\
                      (name='foo_process'...