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Domain-specific languages to Manycore and GPU: Building High-Performance Tools with Python

A tutorial on Domain-Specific Languages

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.

Virtual machine image

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:

  2. Download the machine image itself:

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

    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 | 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.

Software tools

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

Core packages:

Supporting packages:

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)


A tutorial on defining domain-specific languages and transforming them to high-performance code




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