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Problem statement: All compilers have simple profiling-based heuristics to identify and&#13; predict program hot methods and also to make optimization decisions. The major challenge in the&#13; profile-based optimization is addressing the problem of overhead. The aim of this work is to perform&#13; feature subset selection using Genetic Algorithms (GA) to improve and refine the machine learnt static&#13; hot method predictive technique and to compare the performance of the new models against the simple&#13; heuristics. Approach: The relevant features for training the predictive models are extracted from an&#13; initial set of randomly selected ninety static program features, with the help of the GA wrapped with&#13; the predictive model using the Support Vector Machine (SVM), a Machine Learning (ML) algorithm.&#13; Results: The GA-generated feature subsets containing thirty and twenty nine features respectively for&#13; the two predictive models when tested on MiBench predict Long Running Hot Methods (LRHM) and&#13; frequently called hot methods (FCHM) with the respective accuracies of 71% and 80% achieving an&#13; increase of 19% and 22%. Further, inlining of the predicted LRHM and FCHM improve the program&#13; performance by 3% and 5% as against 4% and 6% with Low Level Virtual Machines (LLVM) default&#13; heuristics. When intra-procedural optimizations (IPO) are performed on the predicted hot methods, this&#13; system offers a performance improvement of 5% and 4% as against 0% and 3% by LLVM default&#13; heuristics on LRHM and FCHM respectively. However, we observe an improvement of 36% in certain&#13; individual programs. Conclusion: Overall, the results indicate that the GA wrapped with SVM derived&#13; feature reduction improves the hot method prediction accuracy and that the technique of hot method&#13; prediction based optimization is potentially useful in selective optimization

Feature Subset Selection for Hot Method Prediction using Genetic Algorithm wrapped with Support Vector Machines