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

The reduction operation

Similar to comm.gather, comm.reduce takes an array of input elements in each process and returns an array of output elements to the root process. The output elements contain the reduced result.

In mpi4py, we define the reduction operation through the following statement:

comm.Reduce(sendbuf, recvbuf, rank_of_root_process, op = type_of_reduction_operation)

We must note that the difference with the comm.gather statement resides in the op parameter, which is the operation that you wish to apply to your data, and the mpi4py module contains a set of reduction operations that can be used. Some of the reduction operations defined by MPI are:

  • MPI.MAX: This returns the maximum element

  • MPI.MIN: This returns the minimum element

  • MPI.SUM: This sums up the elements

  • MPI.PROD: This multiplies all elements

  • MPI.LAND: This performs a logical operation and across the elements

  • MPI.MAXLOC: This returns the maximum value and the rank of the process that owns it

  • MPI.MINLOC: This returns the...