Book Image

Python Parallel Programming Cookbook

By : Zaccone
Book Image

Python Parallel Programming Cookbook

By: 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 (8 chapters)
7
Index

Python in a parallel world

To be an interpreted language, Python is fast, and if speed is critical, it easily interfaces with extensions written in faster languages, such as C or C++. A common way of using Python is to use it for the high-level logic of a program; the Python interpreter is written in C and is known as CPython. The interpreter translates the Python code in an intermediate language called Python bytecode, which is analogous to an assembly language, but contains a high level of instruction. While a Python program runs, the so-called evaluation loop translates Python bytecode into machine-specific operations. The use of interpreter has advantages in code programming and debugging, but the speed of a program could be a problem. A first solution is provided by third-party packages, where a programmer writes a C module and then imports it from Python. Another solution is the use of a Just-in-Time Python compiler, which is an alternative to CPython, for example, the PyPy implementation optimizes code generation and the speed of a Python program. In this book, we will examine a third approach to the problem; in fact, Python provides ad hoc modules that could benefit from parallelism. The description of many of these modules, in which the parallel programming paradigm falls, will be discussed in subsequent chapters.

However, in this chapter, we will introduce the two fundamental concepts of threads and processes and how they are addressed in the Python programming language.