Chapter 1, Getting Started with Parallel Computing and Python, gives you an overview of parallel programming architectures and programming models. This chapter introduces the Python programming language, the characteristics of the language, its ease of use and learning, extensibility, and richness of software libraries and applications. It also shows you how to make Python a valuable tool for any application, and also, of course, for parallel computing.

Chapter 2, Thread-based Parallelism, discusses thread parallelism using the threading Python module. Through complete programming examples, you will learn how to synchronize and manipulate threads to implement your multithreading applications.

Chapter 3, Process-based Parallelism, will guide through the process-based approach to parallelize a program. A complete set of examples will show you how to use the multiprocessing Python module. Also, this chapter will explain how to perform communication through processes, using the message passing parallel programming paradigm via the mpi4py Python module.

Chapter 4, Asynchronous Programming, explains the asynchronous model for concurrent programming. In some ways, it is simpler than the threaded one because there is a single instruction stream and tasks explicitly relinquish control instead of being suspended arbitrarily. This chapter will show you how to use the Python asyncio module to organize each task as a sequence of smaller steps that must be executed in an asynchronous manner.

Chapter 5, Distributed Python, introduces you to distributed computing. It is the process of aggregating several computing units logically and may even be geographically distributed to collaboratively run a single computational task in a transparent and coherent way. This chapter will present some of the solutions proposed by Python for the implementation of these architectures using the OO approach, Celery, SCOOP, and remote procedure calls, such as Pyro4 and RPyC. It will also include different approaches, such as PyCSP, and finally, Disco, which is the Python version of the MapReduce algorithm.

Chapter 6, GPU Programming with Python, describes the modern Graphics Processing Units (GPUs) that provide breakthrough performance for numerical computing at the cost of increased programming complexity. In fact, the programming models for GPUs require the programmer to manually manage the data transfer between a CPU and GPU. This chapter will teach you, through the programming examples and use cases, how to exploit the computing power provided by the GPU cards, using the powerful Python modules: PyCUDA, NumbaPro, and PyOpenlCL.

All the examples of this book can be tested in a Windows 7 32-bit machine. Also, a Linux environment will be useful.

The Python versions needed to run the examples are:

The following modules (all of which are freely downloadable) are required:

This book is intended for software developers who want to use parallel programming techniques to write powerful and efficient code. After reading this book, you will be able to master the basics and the advanced features of parallel computing. The Python programming language is easy to use and allows nonexperts to deal with and easily understand the topics exposed in this book.

This book contains the following sections:

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, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "To execute this first example, we need the program helloPythonWithThreads.py."

A block of code is set as follows:

print ("Hello Python Parallel Cookbook!!")
closeInput = raw_input("Press ENTER to exit")
print "Closing calledProcess"

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

@asyncio.coroutine
def factorial(number):
do Something

@asyncio.coroutine

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

C:\>mpiexec -n 4 python virtualTopology.py

New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "Open an admin Command Prompt by right-clicking on the command prompt icon and select Run as administrator."

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