Book Image

Learning Concurrency in Python

By : Elliot Forbes
Book Image

Learning Concurrency in Python

By: Elliot Forbes

Overview of this book

Python is a very high level, general purpose language that is utilized heavily in fields such as data science and research, as well as being one of the top choices for general purpose programming for programmers around the world. It features a wide number of powerful, high and low-level libraries and frameworks that complement its delightful syntax and enable Python programmers to create. This book introduces some of the most popular libraries and frameworks and goes in-depth into how you can leverage these libraries for your own high-concurrent, highly-performant Python programs. We'll cover the fundamental concepts of concurrency needed to be able to write your own concurrent and parallel software systems in Python. The book will guide you down the path to mastering Python concurrency, giving you all the necessary hardware and theoretical knowledge. We'll cover concepts such as debugging and exception handling as well as some of the most popular libraries and frameworks that allow you to create event-driven and reactive systems. By the end of the book, you'll have learned the techniques to write incredibly efficient concurrent systems that follow best practices.
Table of Contents (20 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Index

PyOpenCL


OpenCL, or Open Computing Language in its full form, is a low-level API for heterogeneous computing that runs on CUDA-powered GPUs. PyOpenCL represents the Python implementation of this API that enables us to write Python applications that leverage the power of a whole range of different platforms from CPUs, GPUs, DSPs and FPGAs.

Note

The official documentation for the PyOpenCL library can be found athttps://documen.tician.de/pyopencl/.

Example

Let's dissect the example that comes in the official documentation. This will act as a perfect starting point, which we can expand upon later.  

We will first import all the necessary modules at the top and alias NumPy and PyOpenCL as np and cl, respectively. We will then generate two random numbers of type numpy.float32. We will then create some context within our PyOpenCL program, by calling this with no parameters. This function will interactively allow you to choose what platforms and devices that your program should run on top of, which is...