Return to search

Applying support vector machines to discover just-in-time method-specific compilation strategies

Adaptive Just-in-Time compilers employ multiple techniques to concentrate compilation efforts in the most promising spots of the application, balancing tight compilation budgets with an appropriate level of code quality. Some compiler researchers propose that Just-in-Time compilers should benefit from method-specific compilation strategies. These strategies can be discovered through machine-learning techniques, where a compilation strategy is tailored to a method based on the method's characteristics. This thesis investigates the use of Support Vector Machines in Testarossa, a commercial Just-in-Time compiler employed in the IBM J9 Java Virtual Machine. This new infrastructure allows Testarossa to explore numerous compilation strategies, generating the data needed for training such models. The infrastructure also integrates Testarossa to learned models that predict which compilation strategy balances code quality and compilation effort, on a per-method basis. The thesis also presents the results of an extensive experimental evaluation of the infrastructure and compares these results with the performance of the original Testarossa.

Identiferoai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:AEU.10048/1532
Date11 1900
CreatorsNabinger Sanchez, Ricardo
ContributorsAmaral, Jos Nelson (Computing Science), Szafron, Duane (Computing Science), Sutton, Richard S. (Computing Science), Cockburn, Bruce F. (Electrical and Computer Engineering)
Source SetsLibrary and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada
LanguageEnglish
Detected LanguageEnglish
TypeThesis
Format796553 bytes, application/pdf
RelationSanchez, Ricardo Nabinger; Amaral, Jos Nelson; Szafron, Duane; Pirvu, Marius; Stoodley, Mark (2010). http://sbac-pad-2010.lncc.br/

Page generated in 0.0021 seconds