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 Distributing tasks to different cores

When executed on a multicore computer, we can think of it that mpiexec copies the given Python script to the number of cores and runs each copy. As an example, consider the one-liner script print_me.py with the command print("Hello it's me"), that, when executed with mpiexec -n 4 print_me.py, generates the same message on the screen four times, each sent from a different core.

In order to be able to execute different tasks on different cores, we have to be able to distinguish these cores in the script.

To this end, we create a so-called communicator instance, which organizes the communication between the world, that is, the input and output units like the screen, the keyboard, or a file, and the individual cores. Furthermore, the individual cores are given identifying numbers, called a rank:

from mpi4py import MPI
comm=MPI.COMM_WORLD # making a communicator instance
rank=comm.Get_rank() # querrying for the numeric...