A Relearning Virtual Machine for Hot Method Prediction

Predicting hot methods of a program code using machine learning algorithms in compiler optimization eliminates the overhead incurred during runtime identification. Since learning is a continuous process, the system should be able to relearn and update itself. In this study we implement this idea in a virtual machine which learns and relearns as to how hot methods can be effectively predicted in a program. This is the first attempt to make a compilation system relearn about hot method prediction after each execution. By applying relearning we are able to develop models for the prediction of the frequently called and the long running hot methods that can obtain 19 and 37% accuracies showing an improvement of 10 and 21%, respectively over the corresponding models without relearning. This is due to the ability of the model to learn from every program that enters execution and reconstruct itself.