Research questions

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?

Principle Component Analysis has been used to evaluate how much each feature varies across BEEBS. See blog post here: http://mageec.org/2014/08/11/evaluation-of-static-program-features-used-in-mageec/#more-408

• 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?