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VESPA: static profiling for binary optimization

Authors:

Angélica Aparecida Moreira,

Guilherme Ottoni,

Fernando Magno Quintão PereiraAuthors Info & Claims

Proceedings of the ACM on Programming Languages, Volume 5, Issue OOPSLA

Article No.: 144, Pages 1 - 28

https://doi.org/10.1145/3485521

Published: 15 October 2021 Publication History

Abstract

Over the past few years, there has been a surge in the popularity of binary optimizers such as BOLT, Propeller, Janus and HALO. These tools use dynamic profiling information to make optimization decisions. Although effective, gathering runtime data presents developers with inconveniences such as unrepresentative inputs, the need to accommodate software modifications, and longer build times. In this paper, we revisit the static profiling technique proposed by Calder et al. in the late 90’s, and investigate its application to drive binary optimizations, in the context of the BOLT binary optimizer, as a replacement for dynamic profiling. A few core modifications to Calder et al.’s original proposal, consisting of new program features and a new regression model, are sufficient to enable some of the gains obtained through runtime profiling. An evaluation of BOLT powered by our static profiler on four large benchmarks (clang, GCC, MySQL and PostgreSQL) yields binaries that are 5.47 % faster than the executables produced by clang -O3.

Supplementary Material

Auxiliary Presentation Video (oopsla21main-p194-p-video.mp4)

This is a presentation video of my talk at OOPSLA 2021 of our accepted paper VESPA: Static Profiling for Binary Optimization. In this paper we explored the use of static profiles inferred by a machine learning model in the context of binary optimization. An evaluation of the binary optimizer BOLT powered by our static profiler on four large benchmarks (clang, GCC, MySQL and PostgreSQL) yields binaries that are 5.47% faster than the executables produced by clang -O3.

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Cited By

Borisov PKosolapov Y(2024)A Method to Quantitative Compare Obfuscating TtransformationsСпособ количественного сравнения обфусцирующих преобразованийInformatics and AutomationИнформатика и автоматизация10.15622/ia.23.3.323:3(684-726)Online publication date: 28-May-2024
https://doi.org/10.15622/ia.23.3.3
Frenot LPereira FRodríguez GSadayappan PSukumaran-Rajam A(2024)Reducing the Overhead of Exact Profiling by Reusing Affine VariablesProceedings of the 33rd ACM SIGPLAN International Conference on Compiler Construction10.1145/3640537.3641569(150-161)Online publication date: 17-Feb-2024
https://dl.acm.org/doi/10.1145/3640537.3641569
Čugurović MVujošević Janičić MJovanović VWürthinger T(2024)GraalSP: Polyglot, efficient, and robust machine learning-based static profilerJournal of Systems and Software10.1016/j.jss.2024.112058213(112058)Online publication date: Jul-2024
https://doi.org/10.1016/j.jss.2024.112058
Show More Cited By

Index Terms

VESPA: static profiling for binary optimization
1. Computing methodologies
  1. Machine learning
2. Software and its engineering
  1. Software notations and tools
    1. Compilers

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cover image Proceedings of the ACM on Programming Languages

Proceedings of the ACM on Programming Languages Volume 5, Issue OOPSLA

October 2021

2001 pages

EISSN:2475-1421

DOI:10.1145/3492349

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Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 October 2021

Published in PACMPL Volume 5, Issue OOPSLA

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Fundação de Amparo à Pesquisa do Estado de Minas Gerais
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Cited By

Borisov PKosolapov Y(2024)A Method to Quantitative Compare Obfuscating TtransformationsСпособ количественного сравнения обфусцирующих преобразованийInformatics and AutomationИнформатика и автоматизация10.15622/ia.23.3.323:3(684-726)Online publication date: 28-May-2024
https://doi.org/10.15622/ia.23.3.3
Frenot LPereira FRodríguez GSadayappan PSukumaran-Rajam A(2024)Reducing the Overhead of Exact Profiling by Reusing Affine VariablesProceedings of the 33rd ACM SIGPLAN International Conference on Compiler Construction10.1145/3640537.3641569(150-161)Online publication date: 17-Feb-2024
https://dl.acm.org/doi/10.1145/3640537.3641569
Čugurović MVujošević Janičić MJovanović VWürthinger T(2024)GraalSP: Polyglot, efficient, and robust machine learning-based static profilerJournal of Systems and Software10.1016/j.jss.2024.112058213(112058)Online publication date: Jul-2024
https://doi.org/10.1016/j.jss.2024.112058
Ugur MJiang CErf AAhmed Khan TKasikci B(2022)One Profile Fits AllACM SIGOPS Operating Systems Review10.1145/3544497.354450256:1(26-33)Online publication date: 14-Jun-2022
https://dl.acm.org/doi/10.1145/3544497.3544502
Khan TUgur MNathella KSunwoo DLitz HJiménez DKasikci BHardavellas NCampanoni SGrot BKarpuzcu U(2022)Whisper: Profile-Guided Branch Misprediction Elimination for Data Center ApplicationsProceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture10.1109/MICRO56248.2022.00017(19-34)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1109/MICRO56248.2022.00017

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