Book Image

Python Parallel Programming Cookbook - Second Edition

By : Giancarlo Zaccone
Book Image

Python Parallel Programming Cookbook - Second Edition

By: Giancarlo Zaccone

Overview of this book

<p>Nowadays, it has become extremely important for programmers to understand the link between the software and the parallel nature of their hardware so that their programs run efficiently on computer architectures. Applications based on parallel programming are fast, robust, and easily scalable. </p><p> </p><p>This updated edition features cutting-edge techniques for building effective concurrent applications in Python 3.7. The book introduces parallel programming architectures and covers the fundamental recipes for thread-based and process-based parallelism. You'll learn about mutex, semaphores, locks, queues exploiting the threading, and multiprocessing modules, all of which are basic tools to build parallel applications. Recipes on MPI programming will help you to synchronize processes using the fundamental message passing techniques with mpi4py. Furthermore, you'll get to grips with asynchronous programming and how to use the power of the GPU with PyCUDA and PyOpenCL frameworks. Finally, you'll explore how to design distributed computing systems with Celery and architect Python apps on the cloud using PythonAnywhere, Docker, and serverless applications. </p><p> </p><p>By the end of this book, you will be confident in building concurrent and high-performing applications in Python.</p>
Table of Contents (16 chapters)
Title Page

GPU programming with Numba

Numba is a Python compiler that provides CUDA-based APIs. It has been designed primarily for numerical computing tasks, just like the NumPy library. In particular, the numba library manages and processes the array data types provided by NumPy.

In fact, the exploitation of data parallelism, which is inherent in numerical computation involving arrays, is a natural choice for GPU accelerators.

The Numba compiler works by specifying the signature types (or decorators) for Python functions and enabling the compilation at runtime (this type of compilation is also called Just In Time).

The most important decorators are as follows:

  • jit: This allows the developer to write CUDA-like functions. When encountered, the compiler translates the code under the decorator into the pseudo-assembly PTX language, so that it can be executed by the GPU.
  • autojit...