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

18.3.4 Blocking and non-blocking communication

The commands send and recv and their buffer counterparts Send and Recv are so-called blocking commands. That means a command send is completed when the corresponding send buffer is freed. When this will happen depends on several factors such as the particular communication architecture of your system and the amount of data that is to be communicated. Finally, the command send is considered to be freed when the corresponding command recv has got all the information. Without such a command recv, it will wait forever. This is called a deadlock situation.

The following script demonstrates a situation with the potential for deadlock. Both processes send simultaneously. If the amount of data to be communicated is too big to be stored the command send is waiting for a corresponding recv to empty the pipe, but recv never is invoked due to the waiting state. That's a deadlock.

from mpi4py...