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You're reading from Mastering Python Scientific Computing A complete guide for Python programmers to master scientific computing using Python APIs and tools

Product type Paperback

Published in Sep 2015

Publisher

ISBN-13 9781783288823

Length 300 pages

Edition 1st Edition

Languages

Python

Tools

NumPy

Concepts

Data Analysis

Author (1):

Kumar Mehta

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Table of Contents (12) Chapters

Preface

1. The Landscape of Scientific Computing – and Why Python?

2. A Deeper Dive into Scientific Workflows and the Ingredients of Scientific Computing Recipes FREE CHAPTER

3. Efficiently Fabricating and Managing Scientific Data

4. Scientific Computing APIs for Python

5. Performing Numerical Computing

6. Applying Python for Symbolic Computing

7. Data Analysis and Visualization

8. Parallel and Large-scale Scientific Computing

9. Revisiting Real-life Case Studies

10. Best Practices for Scientific Computing

Index

The architecture of IPython parallel computing

The architecture of parallel computing in IPython has three main components. These components are part of the parallel package of IPython. The architecture of IPython parallel computing is depicted in the following figure:

The three main components of IPython parallel computing are client, controller, and engines. The controller component is composed of two subcomponents: HUB and SCHEDULERS. It allows client interaction with engines through two main interfaces: direct interface and load-balanced interface.

The components of parallel computing

Various components and concepts related to the IPython parallel computing architecture will be discussed in this subsection. The components are the IPython engine, the IPython controller (the hub and schedulers), and the IPython clients and views.

The IPython engine

The core component performs the actual execution of the Python command received as a network request. The engine is an instance of a regular Python...