Book Image

Scientific Computing with Python - Second Edition

By : Claus Führer, Jan Erik Solem, Olivier Verdier
Book Image

Scientific Computing with Python - Second Edition

By: Claus Führer, Jan Erik Solem, Olivier Verdier

Overview of this book

Python has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python. This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations. By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing.
Table of Contents (23 chapters)
20
About Packt
22
References

18.3.2 Point-to-point communication

Point-to-point communication directs information flow from one process to a designated receiving process. We first describe the methods and features by considering a ping-pong situation and a telephone-chain situation and explain the notion of blocking.

Point-to-point communication is applied in scientific computing, for instance in random-walk or particle-tracing applications on domains that are divided into a number of subdomains corresponding to the number of processes that can be carried out in parallel.

The ping-pong example assumes that we have two processors sending an integer back and forth to each other and increasing its value by one.

We start by creating a communicator object and checking that we have two processes available:

from mpi4py import MPI
comm=MPI.COMM_WORLD # making a communicator instance
rank=comm.Get_rank() # querying for the numeric identifier of the core
size=comm.Get_size() # the total number of cores assigned
if not (size...