Difference between revisions of "Research questions"

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(Machine Learning)
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Revision as of 17:30, 14 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?


  • 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?
  • How varied a set of benchmarks is needed to properly train a database?
  • Can fewer benchmarks be used, but each benchmark altered by applying a random set of transformations?

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?