Version 5 (modified by hartono, 15 years ago) (diff)


ORIO -- An Annotation-Based Performance Tuning Tool


Orio is an extensible annotation system, implemented in Python, that aims at improving both performance and productivity by enabling software developers to insert annotations into their source code (in C/C++) that trigger a number of low-level performance optimizations on a specified code fragment. The tool generates many tuned versions of the same operation using different optimization parameters, and performs an empirical search for selecting the best among multiple optimized code variants.


Orio 0.0.1 (Alpha)


The Orio installation follows the standard Python Module Distribution Utilities, or Disutils for short.

For users who want to quickly install Orio to the standard locations of third-party Python modules (requiring superuser privileges in a Unix system), the installation is simple as shown below.

  1. tar -xvzf orio.tar.gz
  2. cd orio
  3. python install

On a Unix platform, the above install command puts an orcc script in the /usr/bin location, and also creates an orio module directory in the /usr/lib/python2.X/site-packages location.

In order to install Orio to an alternate location, users need to supply a base directory for the installation. For instance, the following install command

  1. tar -xvzf orio.tar.gz
  2. cd orio
  3. python install --prefix=/home/username

To test whether Orio has been properly installed in your system, try to execute orcc command as given below as an example.

% orcc --help

description: compile shell for Orio

usage: orcc [options] <ifile> 
  <ifile>   input file containing the annotated code

  -h, --help                     display this message
  -o <file>, --output=<file>     place the output to <file>
  -v, --verbose                  verbosely show details of the results of the running program


Currently under construction


  • Boyana Norris, Albert Hartono, and William Gropp. "Annotations for Productivity and Performance Portability," in Petascale Computing: Algorithms and Applications. Computational Science. Chapman & Hall / CRC Press, Taylor and Francis Group, 2007. Preprint ANL/MCS-P1392-0107. (bib, pdf)