Ubiquitous Computing and Communication Journal
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Abstract
Title: HOT METHOD PREDICTION USING SUPPORT VECTOR MACHINES
Authors: Mrs. Sandra Johnson, Mrs. Valli Shanmugam
Abstract:
Runtime hot method detection being an important dynamic compiler optimization parameter, has challenged researchers to explore and refine techniques to address the problem of expensive profiling overhead incurred during the process. Although the recent trend has been toward the application of machine learning heuristics in compiler optimization, its role in identification and prediction of hot methods has been ignored. The aim of this work is to develop a model using the machine learning algorithm, the Support Vector Machine (SVM) to identify and predict hot methods in a given program, to which the best set of optimizations could be applied. When trained with ten static program features, the derived model predicts hot methods with an appreciable 62.57% accuracy.