Article

Using machines to learn method-specific compilation strategies

Authors:

Ricardo Nabinger Sanchez,

Jose Nelson Amaral,

Mark StoodleyAuthors Info & Claims

CGO '11: Proceedings of the 9th Annual IEEE/ACM International Symposium on Code Generation and Optimization

Pages 257 - 266

Published: 02 April 2011 Publication History

Abstract

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.

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Verma SKaur HPirvu MKent KRosà AHenz M(2023)Array Bytecode Support in MicroJITProceedings of the 15th ACM SIGPLAN International Workshop on Virtual Machines and Intermediate Languages10.1145/3623507.3623557(29-38)Online publication date: 18-Oct-2023
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Mosaner RLeopoldseder DKisling WStadler LMössenböck H(2022)Machine-Learning-Based Self-Optimizing Compiler Heuristics✱Proceedings of the 19th International Conference on Managed Programming Languages and Runtimes10.1145/3546918.3546921(98-111)Online publication date: 14-Sep-2022
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Using machines to learn method-specific compilation strategies
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cover image ACM Conferences

CGO '11: Proceedings of the 9th Annual IEEE/ACM International Symposium on Code Generation and Optimization

April 2011

324 pages

ISBN:9781612843568

Sponsors

SIGMICRO: ACM Special Interest Group on Microarchitectural Research and Processing

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IEEE Computer Society

United States

Publication History

Published: 02 April 2011

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CGO '11 Paper Acceptance Rate 28 of 105 submissions, 27%;

Overall Acceptance Rate 312 of 1,061 submissions, 29%

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Cited By

Verma SKaur HPirvu MKent KRosà AHenz M(2023)Array Bytecode Support in MicroJITProceedings of the 15th ACM SIGPLAN International Workshop on Virtual Machines and Intermediate Languages10.1145/3623507.3623557(29-38)Online publication date: 18-Oct-2023
https://dl.acm.org/doi/10.1145/3623507.3623557
Mosaner RLeopoldseder DKisling WStadler LMössenböck H(2022)Machine-Learning-Based Self-Optimizing Compiler Heuristics✱Proceedings of the 19th International Conference on Managed Programming Languages and Runtimes10.1145/3546918.3546921(98-111)Online publication date: 14-Sep-2022
https://dl.acm.org/doi/10.1145/3546918.3546921
Ashouri AKillian WCavazos JPalermo GSilvano C(2018)A Survey on Compiler Autotuning using Machine LearningACM Computing Surveys10.1145/319797851:5(1-42)Online publication date: 18-Sep-2018
https://dl.acm.org/doi/10.1145/3197978
Garciarena USantana RNeumann FSutton A(2016)Evolutionary Optimization of Compiler Flag Selection by Learning and Exploiting Flags InteractionsProceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion10.1145/2908961.2931696(1159-1166)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908961.2931696
Shingarov BLaval JEtien A(2015)Live Introspection of Target-Agnostic JIT in SimulationProceedings of the International Workshop on Smalltalk Technologies10.1145/2811237.2811295(1-9)Online publication date: 15-Jul-2015
https://dl.acm.org/doi/10.1145/2811237.2811295
Nobre RMartins LCardoso JCorporaal HStuijk S(2015)Use of Previously Acquired Positioning of Optimizations for Phase Ordering ExplorationProceedings of the 18th International Workshop on Software and Compilers for Embedded Systems10.1145/2764967.2764978(58-67)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1145/2764967.2764978
Martins LNobre RDelbem AMarques ECardoso J(2014)Exploration of compiler optimization sequences using clustering-based selectionACM SIGPLAN Notices10.1145/2666357.259782149:5(63-72)Online publication date: 12-Jun-2014
https://dl.acm.org/doi/10.1145/2666357.2597821
Magni ADubach CO'Boyle MAmaral JTorrellas J(2014)Automatic optimization of thread-coarsening for graphics processorsProceedings of the 23rd international conference on Parallel architectures and compilation10.1145/2628071.2628087(455-466)Online publication date: 24-Aug-2014
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Martins LNobre RDelbem AMarques ECardoso JZhang YKulkarni P(2014)Exploration of compiler optimization sequences using clustering-based selectionProceedings of the 2014 SIGPLAN/SIGBED conference on Languages, compilers and tools for embedded systems10.1145/2597809.2597821(63-72)Online publication date: 12-Jun-2014
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Jantz MKulkarni P(2013)Exploring single and multilevel JIT compilation policy for modern machines 1ACM Transactions on Architecture and Code Optimization10.1145/2541228.254122910:4(1-29)Online publication date: 1-Dec-2013
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