Authors: Sandra Johnson, Valli Shanmugam
Addresses: Department of Information Technology, R.M.K. Engineering College, R.S.M. Nagar, Kavaraipettai – 601 206, India Chennai, Tamil Nadu, India. ' Department of Computer Science and Engineering, College of Engineering, Guindy, Anna University, Chennai – 600 025, Tamil Nadu, India
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.
Keywords: hot method prediction; machine learning; support vector machines; SVM; feature subset selection; virtual machines; optimisation; hot methods; target segments; modelling.
International Journal of Computational Science and Engineering, 2011 Vol.6 No.3, pp.192 - 205
Available online: 20 Aug 2011 *Full-text access for editors Access for subscribers Purchase this article Comment on this article