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

GPU programming with NumbaPro

NumbaPro is a Python compiler that provides a CUDA-based API to write CUDA programs. It is designed for array-oriented computing tasks, much like the widely used NumPy library. The data parallelism in array-oriented computing tasks is a natural fit for accelerators such as GPUs. NumbaPro understands NumPy array types and uses them to generate efficient compiled code for execution on GPUs or multicore CPUs.

The compiler works by allowing you to specify type signatures for Python functions, which enable compilation at runtime (called the JIT compilation).

The most important decorators are:

  • numbapro.jit: This allows a developer to write CUDA-like functions. When encountered, the compiler translates the code under the decorator into the pseudo assembly PTX language to be executed in the GPU.

  • numbapro.autojit: This annotates a function for a deferred compilation procedure. This means that each function with this signature is compiled exactly once.

  • numbapro.vectorize...