RT Journal Article
JF Proceedings of the 2013 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)
YR 2011
VO 00
SP 257
TI Using machines to learn method-specific compilation strategies
A1 Duane Szafron,
A1 Ricardo Nabinger Sanchez,
A1 Marius Pirvu,
A1 Mark Stoodley,
A1 Jose Nelson Amaral,
AB Support Vector Machines (SVMs) are used to discover method-specific compilation strategies in Testarossa, a commercial Just-in-Time (JiT) compiler employed in the IBM® J9 Java™ Virtual Machine. The learning process explores a large number of different compilation strategies to generate the data needed for training models. The trained machine-learned model is integrated with the compiler to predict a compilation plan that balances code quality and compilation effort on a per-method basis. The machine-learned plans outperform the original Testarossa for start-up performance, but not for throughput performance, for which Testarossa has been highly hand-tuned for many years.
PB IEEE Computer Society, [URL:http://www.computer.org]
LA English
DO 10.1109/CGO.2011.5764693
LK http://doi.ieeecomputersociety.org/10.1109/CGO.2011.5764693