adaptive java optimisation using instance-based learning
This paper describes a portable,machine learning-based approach to Java optimisation. This approach uses an instance-based learning scheme to select good transformations drawn from Pugh 's Unified Transformation Framework [11]. This approach was implemented and applied to a number of numerical Java benchmarks on two platforms. Using this scheme, we are able to gain over 70\% of the performance improvement found when using an exhaustive iterative search of the best compiler optimisations. Thus we have a scheme that gives a high level of portable performance without any excessive compilations.
adapt java optimisation instance-based learning learning paper portability scheme select benchmark platform performance iter search compilation  framework  adaptive optimisation optimisation space machine learning