Preface

Months ago, in 2013, I was contacted by Packt Publishing professionals with the mission of writing a book about parallel programming using the Python language. I had never thought of writing a book before and had no idea of the work that was about to come; how complex it would be to conceive this piece of work and how it would feel to fit it into my work schedule within my current job. Although I thought about the idea for over a couple of days, I ended up accepting the mission and said to myself that it will be a great deal of personal learning and a perfect chance to disseminate my knowledge of Python to a worldwide audience, and thus, hopefully leave a worthy legacy along my journey in this life.

The first part of this work is to outline its topics. It is not easy to please everybody; however, I believe I have achieved a good balance in the topics proposed in this mini book, in which I intended to introduce Python parallel programming combining theory and practice. I have taken a risk in this work. I have used a new format to show how problems can be solved, in which examples are defined in the first chapters and then solved by using the tools presented along the length of the book. I think this is an interesting format as it allows the reader to analyze and question the different modules that Python offers.

All chapters combine a bit of theory, thereby building the context that will provide you with some basic knowledge to follow the practical bits of the text. I truly hope this book will be useful for those adventuring into the world of Python parallel programming, for I have tried to focus on quality writing.

What this book covers

Chapter 1, Contextualizing Parallel, Concurrent, and Distributed Programming, covers the concepts, advantages, disadvantages, and implications of parallel programming models. In addition, this chapter exposes some Python libraries to implement parallel solutions.

Chapter 2, Designing Parallel Algorithms, introduces a discussion about some techniques to design parallel algorithms.

Chapter 3, Identifying a Parallelizable Problem, introduces some examples of problems, and analyzes if these problems can be divided into parallel pieces.

Chapter 4, Using the threading and concurrent.futures Modules, explains how to implement each problem presented in Chapter 3, Identifying a Parallelizable Problem, using the threading and concurrent.futures modules.

Chapter 5, Using Multiprocessing and ProcessPoolExecutor, covers how to implement each problem presented in Chapter 3, Identifying a Parallelizable Problem, using multiprocessing and ProcessPoolExecutor.

Chapter 6, Utilizing Parallel Python, covers how to implement each problem presented in Chapter 3, Identifying a Parallelizable Problem, using the parallel Python module.

Chapter 7, Distributing Tasks with Celery, explains how to implement each problem presented in Chapter 3, Identifying a Parallelizable Problem, using the Celery distributed task queue.

Chapter 8, Doing Things Asynchronously, explains how to use the asyncio module and concepts about asynchronous programming.

What you need for this book

Previous knowledge of Python programming is necessary as a Python tutorial will not be included in this book. Knowledge of concurrence and parallel programming is welcome since this book is designed for developers who are getting started in this category of software development. In regards to software, it is necessary to obtain the following:

• Python 3.3 and Python 3.4 (still under development) are required for Chapter 8, Doing Things Asynchronously

• Any code editor of the reader's choice is required

• Parallel Python module 1.6.4 should be installed

• Celery framework 3.1 is required for Chapter 5, Using Multiprocessing and ProcessPoolExecutor

• Any operating system of the reader's choice is required

Who this book is for

This book is a compact discussion about parallel programming using Python. It provides tools for beginner and intermediate Python developers. This book is for those who are willing to get a general view of developing parallel/concurrent software using Python, and to learn different Python alternatives. By the end of this book, you will have enlarged your toolbox with the information presented in the chapters.

Conventions

In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.

Code words in text are shown as follows: "In order to exemplify the use of the multiprocessing.Pipe object, we will implement a Python program that creates two processes, A and B."

A block of code is set as follows:

def producer_task(conn):
    value = random.randint(1, 10)
    conn.send(value)
    print('Value [%d] sent by PID [%d]' % (value, os.getpid()))
    conn.close()

Any command-line input or output is written as follows:

$celery –A tasks –Q sqrt_queue,fibo_queue,webcrawler_queue worker 
--loglevel=info

Note

Warnings or important notes appear in a box like this.

Tip

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