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

Point-to-point communication

One of the most important features among those provided by MPI is the point-to-point communication, which is a mechanism that enables data transmission between two processes: a process receiver, and process sender.

The Python module mpi4py enables point-to-point communication via two functions:

  • Comm.Send(data, process_destination): This sends data to the destination process identified by its rank in the communicator group

  • Comm.Recv(process_source): This receives data from the source process, which is also identified by its rank in the communicator group

The Comm parameter, which stands for communicator, defines the group of processes, that may communicate through message passing:


How to do it…

In the following example, we show you how to utilize the comm.send and comm.recv directives to exchange messages between different processes:

from mpi4py import MPI

rank = comm.rank
print("my rank is : " , rank)

if rank==0: