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2009 International Symposium on Code Generation and Optimization
Cross-Input Learning and Discriminative Prediction in Evolvable Virtual Machines
Seattle, Washington
March 22-March 25
ISBN: 978-0-7695-3576-0
| ASCII Text | x | ||
| Feng Mao, Xipeng Shen, "Cross-Input Learning and Discriminative Prediction in Evolvable Virtual Machines," Proceedings of the 2013 IEEE/ACM International Symposium on Code Generation and Optimization (CGO), pp. 92-101, 2009 International Symposium on Code Generation and Optimization, 2009. | |||
| BibTex | x | ||
| @article{ 10.1109/CGO.2009.10, author = {Feng Mao and Xipeng Shen}, title = {Cross-Input Learning and Discriminative Prediction in Evolvable Virtual Machines}, journal ={Proceedings of the 2013 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)}, volume = {0}, year = {2009}, isbn = {978-0-7695-3576-0}, pages = {92-101}, doi = {http://doi.ieeecomputersociety.org/10.1109/CGO.2009.10}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Proceedings of the 2013 IEEE/ACM International Symposium on Code Generation and Optimization (CGO) TI - Cross-Input Learning and Discriminative Prediction in Evolvable Virtual Machines SN - 978-0-7695-3576-0 SP92 EP101 A1 - Feng Mao, A1 - Xipeng Shen, PY - 2009 KW - Cross-Input Learning KW - Java Virtual Machine KW - Evolvable Computing KW - Adaptive Optimization KW - Input-Centric Optimization KW - Discriminative Prediction VL - 0 JA - Proceedings of the 2013 IEEE/ACM International Symposium on Code Generation and Optimization (CGO) ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CGO.2009.10
Modern languages like Java and C# rely on dynamic optimizations in virtual machines for better performance. Current dynamic optimizations are reactive. Their performance is constrained by the dependence on runtime sampling and the partial knowledge of the execution. This work tackles the problems by developing a set of techniques that make a virtual machine evolve across production runs. The virtual machine incrementally learns the relation between program inputs and optimization strategies so that it proactively predicts the optimizations suitable for a new run. The prediction is discriminative, guarded by confidence measurement through dynamic self-evaluation. We employ an enriched extensible specification language to resolve the complexities in program inputs. These techniques, implemented in Jikes RVM, produce significant performance improvement on a set of Java applications.
Index Terms:
Cross-Input Learning, Java Virtual Machine, Evolvable Computing, Adaptive Optimization, Input-Centric Optimization, Discriminative Prediction
Citation:
Feng Mao, Xipeng Shen, "Cross-Input Learning and Discriminative Prediction in Evolvable Virtual Machines," cgo, pp.92-101, 2009 International Symposium on Code Generation and Optimization, 2009
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