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
About the Author
About the Reviewers

Handling map functions with SCOOP

A common task that is very useful when dealing with lists or other sequences of data is to apply the same operation to each element of the list and then collect the result. For example, a list update may be done in the following way from the Python IDLE:

>>>items = [1,2,3,4,5,6,7,8,9,10]
>>>updated_items = []
>>>for x in items:
>>>    updated_items.append(x*2)

>>> updated_items
>>>  [2, 4, 6, 8, 10, 12, 14, 16, 18, 20]

This is a common operation. However, Python has a built-in feature that does most of the work.

The Python function map(aFunction, aSequence) applies a passed-in function to each item in an iterable object and returns a list containing all the function call results. Now, the same example would be:

>>>items = [1,2,3,4,5,6,7,8,9,10]
>>>def multiplyFor2(x):return x*2
>>>[2, 4, 6, 8, 10, 12, 14, 16, 18, 20]

Here, we...