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

How to use a process pool

The multiprocessing library provides the Pool class for simple parallel processing tasks. The Pool class has the following methods:

  • apply(): It blocks until the result is ready.

  • apply_async(): This is a variant of the apply() method, which returns a result object. It is an asynchronous operation that will not lock the main thread until all the child classes are executed.

  • map(): This is the parallel equivalent of the map() built-in function. It blocks until the result is ready, this method chops the iterable data in a number of chunks that submits to the process pool as separate tasks.

  • map_async(): This is a variant of the map() method, which returns a result object. If a callback is specified, then it should be callable, which accepts a single argument. When the result becomes ready, a callback is applied to it (unless the call failed). A callback should be completed immediately; otherwise, the thread that handles the results will get blocked.

How to do it…

This example...