Effective feature set construction for SVM-based hot method prediction and optimisation Online publication date: Wed, 18-Mar-2015
by Sandra Johnson, Valli Shanmugam
International Journal of Computational Science and Engineering (IJCSE), Vol. 6, No. 3, 2011
Abstract: Optimising compilers rely on profiling to identify the target regions for optimising the input programme. Although profiling is accurate, it incurs a lot of overhead, an obstacle to achieving considerable performance improvement. Alternatively, machine learning-based offline prediction of hot methods that form vital target segments, is bound to eliminate the runtime overhead. In this work, we develop and implement support vector machines-based hot method prediction models trained on effective static programme features generated by a new 'knock-out' algorithm. When trained using low level virtual machine (LLVM) environment, it is possible to predict the frequently called and the long running hot methods with 61% and 68% accuracy. Selective optimisation of the predicted hot methods before programme execution provides substantial performance improvement over default LLVM optimisation.
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