https://github.com/inducer/languages-and-codegen-tutorial

A tutorial on defining domain-specific languages and transforming them to high-performance code
https://github.com/inducer/languages-and-codegen-tutorial

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

• Host: GitHub
• URL: https://github.com/inducer/languages-and-codegen-tutorial
• Owner: inducer
• License: other
• Created: 2015-11-25T22:17:10.000Z (about 10 years ago)
• Default Branch: master
• Last Pushed: 2022-02-08T02:50:36.000Z (almost 4 years ago)
• Last Synced: 2025-03-17T04:06:47.279Z (10 months ago)
• Language: Jupyter Notebook
• Size: 12 MB
• Stars: 81
• Watchers: 8
• Forks: 15
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

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README

          
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:

   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.

Software tools

--------------

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.

License

-------

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)