Book Image

Mastering Python Scientific Computing

Book Image

Mastering Python Scientific Computing

Overview of this book

Table of Contents (17 chapters)
Mastering Python Scientific Computing
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Summary


In this chapter, we discussed the concepts of high-performance scientific computing using IPython. We started the discussion from the basic concepts of parallel computing. After the basic concepts, we discussed the detailed architecture of IPython parallel computing. Later, we discussed the development of sample parallel programs, IPython magic functions, and parallel decorators.

We also covered the advanced features of IPython: fault tolerance, dynamic load balancing, managing dependencies among tasks, object movement between clients and engines, IPython database support, using MPI from IPython, and managing the Amazon EC2 cluster using StarCluster from IPython. Then we discussed multiprocessing and multithreading in Python. At the end, we covered the development of distributed applications using Hadoop and Spark in Python.

In the next chapter, we will discuss several real-life case studies of using Python's tools/APIs for scientific computing. We will consider applications from various...