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

• Author / Creator

  Nabinger Sanchez, Ricardo

• 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.

• Subjects / Keywords

  • Method-specific
  • Just-in-time compilation
  • Machine-learning
  • Optimizing compiler

• Graduation date

  Fall 2010

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R37W4H

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.