Parallel Python

Welcome to a short course that will teach you how to write Python scripts that can take advantage of the processing power of multicore processors and large compute clusters. While this course is based on Python, the core ideas of functional programming and parallel functional programming are applicable to a wide range of languages.

To follow this course you should already have a good basic understanding of Python, e.g. loops, functions, containers and classes. This course will rely on you understanding the material presented in the Beginning Python and Intermediate Python courses.

This is a short course that will give you a taste of functional programming and how it can be used to write efficient parallel code. Please work through the course at your own pace. Python is best learned by using it, so please copy out and play with the examples provided, and also have a go at the exercises.

You can jump ahead to any chapter:

1. Part 1: Functional Programming
2. Part 1: Functions as Objects
3. Part 1: Mapping Functions
4. Part 1: Reduction
5. Part 1: Anonymous Functions (lambda)
6. Part 2: Multicore (local) Parallel Programming
7. Part 2: Parallel Python
8. Part 2: Pool
9. Part 2: Parallel map/reduce
10. Part 2: Asynchronous Functions and Futures
11. Part 2: Asynchronous Mapping
12. Part 3: Multi-node (distributed/cluster) Parallel Programming
13. Part 3: Distributed map/reduce
14. Built-in functions
15. Summary

Setup

For the purpose of this course we will be using a free tool called JupyterLab which provides you with a local editor in your web browser where you can write and run Python code. The easiest way to get access to JupyterLab is to install the Anaconda Distribution which is a piece of software which includes Python along with lots of other tools. It is available for Windows, MacOS and Linux.

Once Anaconda is installed, start "Anaconda Navigator" and press the JupyterLab button on the main screen:

This will open JupyterLab in your default web browser and will look something like this:

The way that we will be setting up the space is to have a text editor on the left-hand side of the screen and a terminal on the right hand side. We'll use the editor to write our code and the terminal to run it.

In the launcher tab, scoll down to the "Text File" entry and click that. It will turn the editor into a text editor. Then go to File → New and select "Terminal". It will now have two tabs inside the interface, one labelled "untitled.txt" and the other labelled "Terminal 1":

To make our lives easier, let's rearrange things so that we can see the text editor at the same time as the terminal. Do this by pressing and holding down the left mouse button on the tab that says "Terminal 1" and slowly dragging it to the right-hand side of the window. You'll see a blue outline like this:

Release the mouse button and you'll end up with the two showing side-by-side:

Finally, we want to open a Python Console for doing some interactive exploration in. Open this by going to File → New → Console. When it asks you to select a "kernel", choose Python 3 which should be the default. Drag that tab so that it is overlapping with the "Terminal 1" pane. You should have it looking something like:

This layout is just a recomendation and if you'd rather do it differently, that's fine. Also if you're comfortable using an IDE like VS Code, PyCharm or Spyder then you're welcome to.

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