There are many other high-performance computing techniques than those covered in this chapter. The IPython Cookbook contains many more details. Here is an overview of some of these other techniques:
The Message Passing Interface, or MPI, defines communication protocols for high-performance distributed systems. IPython.parallel has native support for MPI. Here are some other references:
MPI tutorial at http://mpitutorial.com/
MPI with IPython at http://ipython.org/ipython-doc/dev/parallel/parallel_mpi.html
There are many frameworks for distributed computing and big data analysis in Python.
Apache Spark is a big data framework that can run on Hadoop and has a Python API: http://spark.apache.org/.
Dask is a generic and modular parallel computing framework: http://dask.pydata.org/en/latest/.
Let's also mention xray, which provides a labeled array data structure that can work with Dask: http://xray.readthedocs.org/en/stable/.
Bolt...