Abstract:
With a framework like OpenTuner, one could build
domain-specific multi-objective program auto-tuners and gain
significant performance improvements. But explaining why and
interpreting the results are often hard, mainly due to the large
number of parameters and the inability to figure out how each
parameter affects the performance improvement. We have a
solution that can explain the performance improvements by
identifying key parameters while providing better insights on the
tuning process. Our tool uses machine learning techniques to
identify parameters which account for a significant performance
improvement. A user could utilize different methods provided in
the tool to further experiment and verify the accuracy of such
findings. Further, our tool uses multidimensional scaling to
display all the configurations in a two dimensional graph. This
interface allows users to analyze the search space closely and
identify clusters of configurations with good or bad performance.
It also provides real-time information of tuning process which
would help users to optimize the tuning process.
Citation:
H. M. D. Eranjith, I. D. Fernando, G. K. S. Fernando, W. C. M. Soysa and V. S. D. Jayasena, "A visualization and analysis platform for performance tuning," 2016 Moratuwa Engineering Research Conference (MERCon), 2016, pp. 72-77, doi: 10.1109/MERCon.2016.7480118.