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

Using the PyCUDA module


PyCUDA is a Python wrap for Compute Unified Device Architecture (CUDA), the software library developed by NVIDIA for GPU programming. The CUDA programming model is the starting point of understanding how to program the GPU properly with PyCUDA. There are concepts that must be understood and assimilated to be able to approach this tool correctly and to understand the more specific topics that are covered in the following recipes.

A hybrid programming model

The programming model "hybrid" of CUDA (and consequently of PyCUDA, which is a Python wrapper) is implemented through specific extensions to the standard library of the C language. These extensions have been created, whenever possible, syntactically like the function calls in the standard C library. This allows a relatively simple approach to a hybrid programming model that includes the host and device code. The management of the two logical parts is done by the NVCC compiler. Here is a brief description of how this...