Difference between revisions of "Research questions"

From MAGEEC
Jump to: navigation, search
(Created page with "This page covers some research questions that would be interesting to explore, once we have an initial framework to play with ==Dynamic Features== *Can we better predict whi...")
 
Line 13: Line 13:
 
==Interactivity==
 
==Interactivity==
 
*What is the effect of applying N learnt optimizations, and then retaking the features?
 
*What is the effect of applying N learnt optimizations, and then retaking the features?
 +
 +
==Feature Vector==
 +
*Which features should be in the feature vector?
 +
*Are the features compiler specific?
 +
 +
==Machine Learning==
 +
*What types of machine learning performs best for learning optimizations?
 +
*Can the machine learning learn when to 'backtrack' and undo a previously applied optimization, based on benchmark features?
 +
 +
==Data Dependence==
 +
*Do different sequences of optimizations need to be applied for different data sets?
 +
 +
==Multidimensional Cost Functions==
 +
*Can we optimize for energy and performance simultaneously?
 +
*Can the balance between different cost metrics be altered?
 +
*Does the database need to be retrained for different target metrics?

Revision as of 20:17, 4 July 2013

This page covers some research questions that would be interesting to explore, once we have an initial framework to play with

Dynamic Features

  • Can we better predict which optimizations to use if we take dynamic features of the application?
  • What kinds of dynamic features can we capture?
  • GCC/LLVM both have profile guided optimization, can we use this file for the dynamic features?
  • Can hardware counters be used?

Non-binary Parameters

  • Some optimizations have parameters which aren't on or off. Can be learn good values for these parameters?

Interactivity

  • What is the effect of applying N learnt optimizations, and then retaking the features?

Feature Vector

  • Which features should be in the feature vector?
  • Are the features compiler specific?

Machine Learning

  • What types of machine learning performs best for learning optimizations?
  • Can the machine learning learn when to 'backtrack' and undo a previously applied optimization, based on benchmark features?

Data Dependence

  • Do different sequences of optimizations need to be applied for different data sets?

Multidimensional Cost Functions

  • Can we optimize for energy and performance simultaneously?
  • Can the balance between different cost metrics be altered?
  • Does the database need to be retrained for different target metrics?