This tutorial teaches you:

• how to define mathematically-oriented domain-specific languages ("DSLs") in Python
• how to build transformations for your DSLs to take them from abstraction to implementation
• how to generate highly efficient code from your domain-specific language
• how to use just-in-time compilation with OpenCL from Python to execute generated code
• a few existing design studies and use cases for domain-specific languages
• how to use loopy to generate highly efficient code to work with array data targeting heterogeneous processor architectures (CPUs/GPUs)

The tutorial also includes a brief introductory section to familiarize you with the Python and numpy syntax.

This material is an updated version of a tutorial I presented at Supercomputing '15 in Austin.

A virtual machine image is available that has all the necessary tools installed, to allow for easy experimentation. Follow these instructions to get started:

1. Download a version of VirtualBox suitable for your system and install it:

   https://www.virtualbox.org/wiki/Downloads

2. Download the machine image itself:

   http://andreask.cs.illinois.edu/tmp/dsl-tutorial.ova

3. (Optionally) Check whether the image downloaded correctly using the md5sum command line tool (Linux/OS X). On Windows, use this tool:

   http://www.pc-tools.net/win32/md5sums/

   Compare the computed checksum with the following value: 6aa97e046293f8811d1749ab046f7f61

   Only proceed once the two match. If they don't, delete the file and retry the download.

4. Open VirtualBox, click "File > Import Appliance", select the downloaded image and just click "Next" a few times. Once imported, double-click on the virtual machine to make sure it starts. After a little while, you should see a simple desktop environment.
5. Once all these steps complete successfully, congratulations! You are good to go. I'm looking forward to seeing you at the tutorial.

6. Double-click the "Terminal" symbol on the desktop and enter:

   curl -L https://bit.ly/sc15-dsl | bash

   This will download these materials onto the virtual machine and put them into a subdirectory called sc15-tutorial-materials. Next, type:

   ipython3 notebook

   to launch a browser-based interface and get started.

The tutorial demonstrates the use of the following pieces of software:

Core packages:

• Python: https://www.python.org
• numpy: https://www.numpy.org
• pymbolic: https://github.com/inducer/pymbolic
• PyOpenCL: https://github.com/pyopencl/pyopencl
• loopy: https://github.com/inducer/loopy

Supporting packages:

• matplotlib: http://www.matplotlib.org
• mako: http://www.makotemplates.org
• cgen: https://github.com/inducer/cgen

All open-source under MIT/BSD licenses.

Copyright 2015 Andreas Kloeckner

Materials are available for use under a Creative Commons CC-BY license. See included file LICENSE for details. (I.e. by and large: retain authorship information, and otherwise do what you want)