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

18.1 Multicore computers and computer clusters

Most of the modern computers are multicore computers. For example, the laptop used when writing this book has an Intel® i7-8565U processor that has four cores with two threads each.

What does this mean? Four cores on a processor allow performing four computational tasks in parallel. Four cores with two threads each are often counted as eight CPUs by system monitors. For the purposes of this chapter only the number of cores matters.

These cores share a common memory—the RAM of your laptop—and have individual memory in the form of cache memory:

Figure 18.1: A multicore architecture with shared and local cache memory

The cache memory is used optimally by its core and is accessed at high speed, while the shared memory can be accessed by all cores of one CPU. On top, there is the computer's RAM memory and finally, the hard disk, which is also shared memory.

In the next section, we will see how a computational...