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

How to build a PyCUDA application


The PyCUDA programming model is designed for the common execution of a program on a CPU and GPU, so as to allow you to perform the sequential parts on the CPU and the numeric parts, which are more intensive on the GPU. The phases to be performed in the sequential mode are implemented and executed on the CPU (host), while the steps to be performed in parallel are implemented and executed on the GPU (device). The functions to be performed in parallel on the device are called kernels. The steps to execute a generic function kernel on the device are as follows:

  1. The first step is to allocate the memory on the device.

  2. Then we need to transfer data from the host memory to that allocated on the device.

  3. Next, we need to run the device:

    1. Run the configuration.

    2. Invoke the kernel function.

  4. Then, we need to transfer the results from the memory on the device to the host memory.

  5. Finally, release the memory allocated on the device.

    The PyCUDA programming model

How to do it…

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