Book Image

Python Parallel Programming Cookbook - Second Edition

By : Giancarlo Zaccone

Book Image

Python Parallel Programming Cookbook - Second Edition

By: Giancarlo Zaccone

Overview of this book

<p>Nowadays, it has become extremely important for programmers to understand the link between the software and the parallel nature of their hardware so that their programs run efficiently on computer architectures. Applications based on parallel programming are fast, robust, and easily scalable. </p><p> </p><p>This updated edition features cutting-edge techniques for building effective concurrent applications in Python 3.7. The book introduces parallel programming architectures and covers the fundamental recipes for thread-based and process-based parallelism. You'll learn about mutex, semaphores, locks, queues exploiting the threading, and multiprocessing modules, all of which are basic tools to build parallel applications. Recipes on MPI programming will help you to synchronize processes using the fundamental message passing techniques with mpi4py. Furthermore, you'll get to grips with asynchronous programming and how to use the power of the GPU with PyCUDA and PyOpenCL frameworks. Finally, you'll explore how to design distributed computing systems with Celery and architect Python apps on the cloud using PythonAnywhere, Docker, and serverless applications. </p><p> </p><p>By the end of this book, you will be confident in building concurrent and high-performing applications in Python.</p>

Title Page

Copyright and Credits

Copyright and Credits

Python Parallel Programming Cookbook Second Edition

Dedication

About Packt

Contributors

About the author

About the reviewer

Packt is searching for authors like you

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Getting Started with Parallel Computing and Python

Getting Started with Parallel Computing and Python

Why do we need parallel computing?

Flynn's taxonomy

Memory organization

Parallel programming models

Evaluating the performance of a parallel program

Introducing Python

Introducing Python parallel programming

Thread-Based Parallelism

Thread-Based Parallelism

What is a thread?

Python threading module

Defining a thread

Determining the current thread

Defining a thread subclass

Thread synchronization with a lock

Thread synchronization with RLock

Thread synchronization with semaphores

Thread synchronization with a condition

Thread synchronization with an event

Thread synchronization with a barrier

Thread communication using a queue

Process-Based Parallelism

Process-Based Parallelism

Understanding Python's multiprocessing module

Spawning a process

Naming a process

Running processes in the background

Killing a process

Defining processes in a subclass

Using a queue to exchange data

Using pipes to exchange objects

Synchronizing processes

Using a process pool

Message Passing

Message Passing

Technical requirements

Understanding the MPI structure

Using the mpi4py Python module

Implementing point-to-point communication

Avoiding deadlock problems

Collective communication using a broadcast

Collective communication using the scatter function

Collective communication using the gather function

Collective communication using Alltoall

The reduction operation

Optimizing communication

Asynchronous Programming

Asynchronous Programming

Using the concurrent.futures Python module

Managing the event loop with asyncio

Handling coroutines with asyncio

Manipulating tasks with asyncio

Dealing with asyncio and futures

Distributed Python

Distributed Python

Introducing distributed computing

Types of distributed applications

Using the Python socket module

Distributed task management with Celery

Cloud Computing

Cloud Computing

What is cloud computing?

Understanding the cloud computing architecture

Developing web applications with PythonAnywhere

Dockerizing a Python application

Introducing serverless computing

Heterogeneous Computing

Heterogeneous Computing

Understanding heterogeneous computing

Understanding the GPU architecture

Understanding GPU programming

Dealing with PyCUDA

Heterogeneous programming with PyCUDA

Implementing memory management with PyCUDA

Introducing PyOpenCL

Building applications with PyOpenCL

Element-wise expressions with PyOpenCL

Evaluating PyOpenCL applications

GPU programming with Numba

Python Debugging and Testing

Python Debugging and Testing

What is debugging?

What is software testing?

Debugging using Winpdb Reborn

Interacting with pdb

Implementing rpdb for debugging

Dealing with unittest

Application testing using nose

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