Article

Adaptive java optimisation using instance-based learning

Authors:

Michael O'BoyleAuthors Info & Claims

ICS '04: Proceedings of the 18th annual international conference on Supercomputing

Pages 237 - 246

https://doi.org/10.1145/1006209.1006243

Published: 26 June 2004 Publication History

Abstract

This paper describes a portable,machine learning-based approach to Java optimisation. This approach uses an instance-based learning scheme to select good transformations drawn from Pugh 's Unified Transformation Framework [11]. This approach was implemented and applied to a number of numerical Java benchmarks on two platforms. Using this scheme, we are able to gain over 70% of the performance improvement found when using an exhaustive iterative search of the best compiler optimisations. Thus we have a scheme that gives a high level of portable performance without any excessive compilations.

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Cited By

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
Ahmed ULin JSrivastava GMekala MJung H(2022)Fuzzy Active Learning to Detect OpenCL Kernel Heterogeneous Machines in Cyber Physical SystemsIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2022.316715830:11(4618-4629)Online publication date: Nov-2022
https://doi.org/10.1109/TFUZZ.2022.3167158
Ganser SGrößlinger ASiegmund NApel SLengauer C(2018)Speeding up Iterative Polyhedral Schedule Optimization with Surrogate Performance ModelsACM Transactions on Architecture and Code Optimization10.1145/329177315:4(1-27)Online publication date: 19-Dec-2018
https://dl.acm.org/doi/10.1145/3291773
Show More Cited By

Index Terms

Adaptive java optimisation using instance-based learning
1. Computing methodologies
  1. Machine learning
2. Software and its engineering
  1. Software notations and tools
    1. Compilers

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Published In

cover image ACM Conferences

ICS '04: Proceedings of the 18th annual international conference on Supercomputing

June 2004

360 pages

ISBN:1581138393

DOI:10.1145/1006209

General Chair:
Paul Feautrier
LIP, ENS Lyon
,
Program Chairs:
James Goodman
University of Auckland
,
André Seznec
IRISA, INRIA

Copyright © 2004 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 26 June 2004

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ICS04

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ICS04: International Conference on Supercomputing 2004

June 26 - July 1, 2004

Malo, France

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Overall Acceptance Rate 629 of 2,180 submissions, 29%

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Cited By

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
Ahmed ULin JSrivastava GMekala MJung H(2022)Fuzzy Active Learning to Detect OpenCL Kernel Heterogeneous Machines in Cyber Physical SystemsIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2022.316715830:11(4618-4629)Online publication date: Nov-2022
https://doi.org/10.1109/TFUZZ.2022.3167158
Ganser SGrößlinger ASiegmund NApel SLengauer C(2018)Speeding up Iterative Polyhedral Schedule Optimization with Surrogate Performance ModelsACM Transactions on Architecture and Code Optimization10.1145/329177315:4(1-27)Online publication date: 19-Dec-2018
https://dl.acm.org/doi/10.1145/3291773
Kronawitter SLengauer C(2018)Polyhedral Search Space Exploration in the ExaStencils Code GeneratorACM Transactions on Architecture and Code Optimization10.1145/327465315:4(1-25)Online publication date: 10-Oct-2018
https://dl.acm.org/doi/10.1145/3274653
Filho JRodriguez Lda Silva A(2018)Yet Another Intelligent Code-Generating System: A Flexible and Low-Cost SolutionJournal of Computer Science and Technology10.1007/s11390-018-1867-733:5(940-965)Online publication date: 12-Sep-2018
https://doi.org/10.1007/s11390-018-1867-7
Ganser SGrösslinger ASiegmund NApel SLengauer C(2017)Iterative Schedule Optimization for Parallelization in the Polyhedron ModelACM Transactions on Architecture and Code Optimization10.1145/310948214:3(1-26)Online publication date: 22-Aug-2017
https://dl.acm.org/doi/10.1145/3109482
Cosenza BDurillo JErmon SJuurlink B(2017)Autotuning Stencil Computations with Structural Ordinal Regression Learning2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS.2017.102(287-296)Online publication date: May-2017
https://doi.org/10.1109/IPDPS.2017.102
Emani MO'Boyle M(2015)Celebrating diversity: a mixture of experts approach for runtime mapping in dynamic environmentsACM SIGPLAN Notices10.1145/2813885.273799950:6(499-508)Online publication date: 3-Jun-2015
https://dl.acm.org/doi/10.1145/2813885.2737999
Emani MO'Boyle MGrove DBlackburn S(2015)Celebrating diversity: a mixture of experts approach for runtime mapping in dynamic environmentsProceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation10.1145/2737924.2737999(499-508)Online publication date: 3-Jun-2015
https://dl.acm.org/doi/10.1145/2737924.2737999
Junior NFaustino da Silva A(2015)The use of different strategies of search space reduction in mitigation of optimization selection problem2015 Latin American Computing Conference (CLEI)10.1109/CLEI.2015.7360040(1-12)Online publication date: Oct-2015
https://doi.org/10.1109/CLEI.2015.7360040
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