path: root/README

Piglit
------
1. About
2. Setup
3. How to run tests
4. Available test sets
5. How to write tests
6. Todo


1. About
--------

Piglit is a collection of automated tests for OpenGL implementations.

The goal of Piglit is to help improve the quality of open source
OpenGL drivers by providing developers with a simple means to
perform regression tests.

The original tests have been taken from
- Glean ( http://glean.sf.net/ ) and
- Mesa ( http://www.mesa3d.org/ )


2. Setup
--------

First of all, you need to make sure that the following are installed:

  - Python 2.6 or greater
  - numpy (http://www.numpy.org)
  - cmake (http://www.cmake.org)
  - GL, glu and glut libraries and development packages (i.e. headers)
  - X11 libraries and development packages (i.e. headers)
  - libpng, libtiff and related development packages (i.e. headers)

Now configure the build system:

  $ ccmake .

This will start cmake's configuration tool, just follow the onscreen
instructions. The default settings should be fine, but I recommend you:
 - Press 'c' once (this will also check for dependencies) and then
 - Set "CMAKE_BUILD_TYPE" to "Debug"
Now you can press 'c' again and then 'g' to generate the build system.
Now build everything:

  $ make


2.1 Cross Compiling
-------------------

On Linux, if cross-compiling a 32-bit build on a 64-bit host, then you must
invoke cmake with option "-DCMAKE_SYSTEM_PROCESSOR=i386".


2.2 Ubuntu
----------

Install development packages.
  $ sudo apt-get install cmake g++ mesa-common-dev libgl1-mesa-dev libtiff4-dev zlib1g-dev libpng12-dev python-numpy freeglut3-dev x11proto-gl-dev libxrender-dev

Configure and build.
  $ cmake .
  $ make


2.3 Mac OS X
------------

Install CMake. 
http://cmake.org/cmake/resources/software.html
Download and install 'Mac OSX Universal' platform.

Install Xcode.
http://developer.apple.com/xcode

Configure and build.
  $ cmake .
  $ make

glean
In order to build glean, TIFF is required but not included as part of 
Xcode. TIFF must be obtained elsewhere and one solution is to get TIFF 
from MacPorts.

Install MacPorts.
http://www.macports.org/install.php

Install TIFF through MacPorts. The '+universal' option is needed as 
glean cannot be built as x86_64, only as i386.
  $ sudo port selfupdate
  $ sudo port install tiff +universal

glean will also not build with MacOSX10.7.sdk. If you are trying to 
build glean on Mac OS 10.7 (Lion), you will have to use MacOSX10.6.sdk.
  $ ccmake .
Set 'CMAKE_OSX_SYSROOT' to '/Developer/SDKs/MacOSX10.6.sdk'. Configure. 
Generate and exit.
  $ make


2.4 Cygwin
----------

Install development packages.
  - cmake
  - gcc4
  - make
  - opengl
  - libGL-devel
  - libpng12-devel
  - libpng14-devel
  - zlib-devel
  - libtiff-devel
  - python
  - python-numpy
  - libglut-devel
  - libGLU-devel

Configure and build.
  $ cmake .
  $ make


2.5 Windows
-----------

Install Python.
http://www.python.org/download

Install NumPy.
http://sourceforge.net/projects/numpy/files/NumPy

Install CMake.
http://cmake.org/cmake/resources/software.html
Download and install 'Windows' platform.

Install Microsoft Visual Studio.
Install 'Visual C++' feature.

Install TIFF for Windows.
http://gnuwin32.sourceforge.net/packages/tiff.htm

Install Zlib for Windows.
http://gnuwin32.sourceforge.net/packages/zlib.htm

Install LibPng for Windows.
http://gnuwin32.sourceforge.net/packages/libpng.htm

Download OpenGL Core API and Extension Header Files.
http://www.opengl.org/registry/#headers
Copy header files to MSVC.
C:\Program Files\Microsoft Visual Studio 10.0\VC\include\GL

Download freeglut for MSVC.
http://www.transmissionzero.co.uk/software/freeglut-devel

Open Visual Studio Command Prompt.
Start Menu->All Programs->Microsoft Visual Studio 2010->Visual Studio Tools->Visual Studio Command Prompt (2010)
CD to piglit directory.

Run CMake GUI.
  > C:\Program Files\CMake 2.8\bin\cmake-gui.exe .
Configure
  - NMake Makefiles
  - Use default native compilers
Set these variables in the Advanced view.
  - TIFF_INCLUDE_DIR
  - TIFF_LIBRARY
  - ZLIB_INCLUDE_DIR
  - ZLIB_LIBRARY
  - PNG_PNG_INCLUDE_DIR
  - PNG_LIBRARY
  - GLUT_INCLUDE_DIR
  - GLUT_glut_LIBRARY
Configure
Generate
File->Exit

Build from the Visual Studio Command Prompt.
  > nmake


3. How to run tests
-------------------

Make sure that everything is set up correctly:

  $ ./piglit-run.py tests/sanity.tests results/sanity.results

This will run some minimal tests.  If you built Piglit out-of-source, then the
environment variable PIGLIT_BUILD_DIR must be set:

  $ env PIGLIT_BUILD_DIR=/path/to/piglit/build/dir \
    ./piglit-run.py tests/sanity.tests results/sanity.results

Use

  $ ./piglit-run.py

to learn more about the command's syntax. Have a look into the tests/
directory to see what test profiles are available:

  $ cd tests
  $ ls *.tests
     ...
  $ cd ..

See also section 4.

To create some nice formatted test summaries, run

  $ ./piglit-summary-html.py summary/sanity results/sanity.results

Hint: You can combine multiple test results into a single summary.
      During development, you can use this to watch for regressions:

  $ ./piglit-summary-html.py summary/compare results/baseline.results results/current.results

      You can combine as many testruns as you want this way(in theory;
      the HTML layout becomes awkward when the number of testruns increases)

Have a look at the results with a browser:

  $ xdg-open summary/sanity/index.html

The summary shows the 'status' of a test:

 pass  This test has completed successfully.

 warn  The test completed successfully, but something unexpected happened.
       Look at the details for more information.

 fail  The test failed.

 skip  The test was skipped.

[Note: Once performance tests are implemented, 'fail' will mean that the test
       rendered incorrectly or didn't complete, while 'warn' will indicate a
       performance regression]
[Note: For performance tests, result and status will be different concepts.
       While status is always restricted to one of the four values above,
       the result can contain a performance number like frames per second]


4. Available test sets
----------------------

Test sets are specified as Python scripts in the tests directory.
The following test sets are currently available:

sanity.tests
    This suite contains minimal sanity tests. These tests must
    pass, otherwise the other tests will not generate reliable results.

all.tests
    This suite contains all tests.

quick.tests
    Run all tests, but cut down significantly on their runtime
    (and thus on the number of problems they can find).
    In particular, this runs Glean with the --quick option, which
    reduces the number of visuals and state combinations tested.

radeon.tests
r300.tests
r500.tests
    These test suites are adaptations of all.tests, with some tweaks
    to account for hardware limitations in Radeon chips.


5. How to write tests
---------------------

Every test is run as a separate process. This minimizes the impact that
severe bugs like memory corruption have on the testing process.

Therefore, tests can be implemented in an arbitrary standalone language.
I recommend C, C++ and Python, as these are the languages that are already
used in Piglit.

All new tests must be added to the all.tests profile. The test profiles
are simply Python scripts. There are currently two supported test types:

 PlainExecTest
   This test starts a new process and watches the process output (stdout and
   stderr). Lines that start with "PIGLIT:" are collected and interpreted as
   a Python dictionary that contains test result details.

 GleanTest
   This is a test that is only used to integrate Glean tests

Additional test types (e.g. for automatic image comparison) would have to
be added to core.py.

Rules of thumb:
  Test process that exit with a nonzero returncode are considered to have
  failed.

  Output on stderr causes a warning.


6. Todo
-------

 Get automated tests into widespread use ;)

 Automate and integrate tests and demos from Mesa
   Add code that automatically tests whether the test has rendered correctly

 Performance regression tests
   Ideally, this should be done by summarizing / comparing a history of
   test results
   Note that while some small artificial micro-benchmark could be added to
   Piglit, the Phoronix test suite is probably a better place for
   realistic performance testing.