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
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Introduction


The graphics processing unit (GPU) is an electronic circuit that specializes in processing data to render images from polygonal primitives. Although they were designed to carry out rendering images, the GPU has continued to evolve, becoming more complex and efficient in serving both the real-time and offline rendering community and in performing any scientific computations. GPUs are characterized by a highly parallel structure, which allows it to manipulate large datasets in an efficient manner. This feature combined with the rapid improvement in graphics hardware performance and the extent of programmability caught the attention of the scientific world with the possibility of using GPU for purposes other than just rendering images. Traditional GPUs are fixed function devices where the whole rendering pipeline is built on hardware. This restricts graphics programmers, leading them to use different, efficient and high-quality rendering algorithms. Hence, a new GPU was built with...