Article

Rating Compiler Optimizations for Automatic Performance Tuning

Authors:

Rudolf EigenmannAuthors Info & Claims

SC '04: Proceedings of the 2004 ACM/IEEE conference on Supercomputing

Page 14

https://doi.org/10.1109/SC.2004.47

Published: 06 November 2004 Publication History

Abstract

To achieve maximum performance gains through compiler optimization, most automatic performance tuning systems use a feed-back directed approach to rate the code versions generated under different optimization options and to search for the best one. They all face the problem that code versions are only comparable if they run under the same execution context. This paper proposes three accurate, fast and flexible rating approaches that address this problem. The three methods identify comparable execution contexts, model relationships between contexts, or force re-execution of the code under the same context, respectively. We apply these methods in an automatic offline tuning scenario. Our performance tuning system improves the program performance of a selection of SPEC CPU 2000 benchmarks by up to 178% (26% on average). Our techniques reduce program tuning time by up to 96% (80% on average), compared to the state-of-the-art tuning scenario that compares optimization techniques using whole-program execution.

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Cited By

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
Cammarota RNicolau AVeidenbaum AKejariwal ADonato DMadhugiri M(2013)On the determination of inlining vectors for program optimizationProceedings of the 22nd international conference on Compiler Construction10.1007/978-3-642-37051-9_9(164-183)Online publication date: 16-Mar-2013
https://dl.acm.org/doi/10.1007/978-3-642-37051-9_9
Suprapto Wardoyo R(2013)Algorithms of the combination of compiler optimization options for automatic performance tuningProceedings of the 2013 international conference on Information and Communication Technology10.1007/978-3-642-36818-9_10(91-100)Online publication date: 25-Mar-2013
https://dl.acm.org/doi/10.1007/978-3-642-36818-9_10
Show More Cited By

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Information

Published In

cover image ACM Conferences

SC '04: Proceedings of the 2004 ACM/IEEE conference on Supercomputing

November 2004

724 pages

ISBN:0769521533

General Chair:
Jeff Huskamp

Sponsors

SIGARCH: ACM Special Interest Group on Computer Architecture
IEEE-CS: Computer Society

Publisher

IEEE Computer Society

United States

Publication History

Published: 06 November 2004

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SC '04

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SIGARCH
IEEE-CS

SC '04: International Conference for High Performance Computing, Networking, Storage and Analysis

November 6 - 12, 2004

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SC '04 Paper Acceptance Rate 60 of 200 submissions, 30%;

Overall Acceptance Rate 1,516 of 6,373 submissions, 24%

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Cited By

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
Cammarota RNicolau AVeidenbaum AKejariwal ADonato DMadhugiri M(2013)On the determination of inlining vectors for program optimizationProceedings of the 22nd international conference on Compiler Construction10.1007/978-3-642-37051-9_9(164-183)Online publication date: 16-Mar-2013
https://dl.acm.org/doi/10.1007/978-3-642-37051-9_9
Suprapto Wardoyo R(2013)Algorithms of the combination of compiler optimization options for automatic performance tuningProceedings of the 2013 international conference on Information and Communication Technology10.1007/978-3-642-36818-9_10(91-100)Online publication date: 25-Mar-2013
https://dl.acm.org/doi/10.1007/978-3-642-36818-9_10
Chen YFang SHuang YEeckhout LFursin GTemam OWu C(2012)Deconstructing iterative optimizationACM Transactions on Architecture and Code Optimization10.1145/2355585.23555949:3(1-30)Online publication date: 5-Oct-2012
https://dl.acm.org/doi/10.1145/2355585.2355594
Fursin GTemam O(2010)Collective optimizationACM Transactions on Architecture and Code Optimization10.1145/1880043.18800477:4(1-29)Online publication date: 30-Dec-2010
https://dl.acm.org/doi/10.1145/1880043.1880047
Pan ZEigenmann R(2008)PEAK—a fast and effective performance tuning system via compiler optimization orchestrationACM Transactions on Programming Languages and Systems10.1145/1353445.135345130:3(1-43)Online publication date: 21-May-2008
https://dl.acm.org/doi/10.1145/1353445.1353451
Johnson TEigenmann RVijaykumar TYelick KMellor-Crummey J(2007)Speculative thread decomposition through empirical optimizationProceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming10.1145/1229428.1229474(205-214)Online publication date: 14-Mar-2007
https://dl.acm.org/doi/10.1145/1229428.1229474
Pan ZEigenmann RAltman ESkadron KZorn B(2006)Fast, automatic, procedure-level performance tuningProceedings of the 15th international conference on Parallel architectures and compilation techniques10.1145/1152154.1152182(173-181)Online publication date: 16-Sep-2006
https://dl.acm.org/doi/10.1145/1152154.1152182
Pan ZEigenmann R(2006)Fast and Effective Orchestration of Compiler Optimizations for Automatic Performance TuningProceedings of the International Symposium on Code Generation and Optimization10.1109/CGO.2006.38(319-332)Online publication date: 26-Mar-2006
https://dl.acm.org/doi/10.1109/CGO.2006.38

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