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
About the Author
About the Reviewers

Evaluating element-wise expressions with PyOpenCl

Similar to PyCUDA, PyOpenCL provides the functionality in the pyopencl.elementwise class that allows us to evaluate the complicated expressions in a single computational pass. The method that realized this is:

   ElementwiseKernel(context, argument, operation, name,",",", 


  • context: This is the device or the group of devices on which the element-wise operation will be executed

  • argument: This is a C-like argument list of all the parameters involved in the computation

  • operation: This is a string that represents the operation that is to be performed on the argument list

  • name: This is the kernel name associated with ElementwiseKernel

  • optional_parameters: These are not important for this recipe.

How to do it…

In this example, we will again consider the task of adding two integer vectors of 100 elements. The achievement, of course, changes because we use the ElementwiseKernel class, as shown: