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 PyCUDA

The PyCuda.elementwise.ElementwiseKernel function allows us to execute the kernel on complex expressions that are made of one or more operands into a single computational step, which is as follows:


Here, we note that:

  • arguments: This is a C argument list of all the parameters that are involved in the kernel's execution.

  • operation: This is the operation that is to be executed on the specified arguments. If the argument is a vector, each operation will be performed for each entry.

  • name: This is the kernel's name.

  • optional_parameters: These are the compilation directives that are not used in the following example.

How to do it…

In this example, we'll show you the typical use of the ElementwiseKernel call. We have two vectors of 50 elements, input_vector_a and input_vector_b, that are built in a random way. The task here is to evaluate their linear combination.

The code for this is as follows...