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Cole: compiler optimization level exploration

Authors:

Lieven EeckhoutAuthors Info & Claims

CGO '08: Proceedings of the 6th annual IEEE/ACM international symposium on Code generation and optimization

Pages 165 - 174

https://doi.org/10.1145/1356058.1356080

Published: 06 April 2008 Publication History

Abstract

Modern compilers implement a large number of optimizations which all interact in complex ways, and which all have a different impact on code quality, compilation time, code size, energy consumption, etc. For this reason, compilers typically provide a limited number of standard optimization levels, such as -O1, -O2, -O3 and -Os, that combine various optimizations providing a number of trade-offs between multiple objective functions (such as code quality, compilation time and code size). The construction of these optimization levels, i.e., choosing which optimizations to activate at each level, is a manual process typically done using high-level heuristics based on the compiler developer's experience.

This paper proposes COLE, Compiler Optimization Level Exploration, a framework for automatically finding Pareto optimal optimization levels through multi-objective evolutionary searching. Our experimental results using GCC and the SPEC CPU benchmarks show that the automatic construction of optimization levels is feasible in practice, and in addition, yields better optimization levels than GCC's manually derived (-Os, -O1, -O2 and -O3) optimization levels, as well as the optimization levels obtained through random sampling. We also demonstrate that COLE can be used to gain insight into the effectiveness of compiler optimizations as well as to better understand a benchmark's sensitivity to compiler optimizations.

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Cited By

Burgstaller TGarber DLe VFelfernig A(2024)Optimization Space Learning: A Lightweight, Noniterative Technique for Compiler AutotuningProceedings of the 28th ACM International Systems and Software Product Line Conference10.1145/3646548.3672588(36-46)Online publication date: 2-Sep-2024
https://dl.acm.org/doi/10.1145/3646548.3672588
Liang JWu ZFu JWang MSun CJiang YRoychoudhury APaiva AAbreu RStorey M(2024)Mozi: Discovering DBMS Bugs via Configuration-Based Equivalent TransformationProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639112(1-12)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639112
Seeker VCummins CCole MFranke BHazelwood KLeather HGrosser TDubach CSteuwer MXue JOttoni GQuintão Pereira F(2024)Revealing Compiler Heuristics through Automated Discovery and OptimizationProceedings of the 2024 IEEE/ACM International Symposium on Code Generation and Optimization10.1109/CGO57630.2024.10444847(55-66)Online publication date: 2-Mar-2024
https://dl.acm.org/doi/10.1109/CGO57630.2024.10444847
Show More Cited By

Index Terms

Cole: compiler optimization level exploration
1. Software and its engineering
  1. Software notations and tools
    1. Compilers
      1. Source code generation

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Published In

cover image ACM Conferences

CGO '08: Proceedings of the 6th annual IEEE/ACM international symposium on Code generation and optimization

April 2008

235 pages

ISBN:9781595939784

DOI:10.1145/1356058

General Chair:
Mary Lou Soffa
University of Virginia, USA
,
Program Chair:
Evelyn Duesterwald
IBM Research, USA

Copyright © 2008 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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Publication History

Published: 06 April 2008

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CGO '08

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CGO '08: 6th Annual IEEE / ACM International Symposium on Code Generation and Optimization

April 5 - 9, 2008

MA, Boston, USA

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CGO '08 Paper Acceptance Rate 21 of 66 submissions, 32%;

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

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Cited By

Burgstaller TGarber DLe VFelfernig A(2024)Optimization Space Learning: A Lightweight, Noniterative Technique for Compiler AutotuningProceedings of the 28th ACM International Systems and Software Product Line Conference10.1145/3646548.3672588(36-46)Online publication date: 2-Sep-2024
https://dl.acm.org/doi/10.1145/3646548.3672588
Liang JWu ZFu JWang MSun CJiang YRoychoudhury APaiva AAbreu RStorey M(2024)Mozi: Discovering DBMS Bugs via Configuration-Based Equivalent TransformationProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639112(1-12)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639112
Seeker VCummins CCole MFranke BHazelwood KLeather HGrosser TDubach CSteuwer MXue JOttoni GQuintão Pereira F(2024)Revealing Compiler Heuristics through Automated Discovery and OptimizationProceedings of the 2024 IEEE/ACM International Symposium on Code Generation and Optimization10.1109/CGO57630.2024.10444847(55-66)Online publication date: 2-Mar-2024
https://dl.acm.org/doi/10.1109/CGO57630.2024.10444847
Ahmed HFahim Ul Haque MRaza Khan HNadeem GArshad KAssaleh KCesar Santos P(2024)Selecting the Best Compiler Optimization by Adopting Natural Language ProcessingIEEE Access10.1109/ACCESS.2024.345151612(121700-121711)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3451516
Ahmed HHyder MHaque MSantos P(2024)Exploring Compiler Optimization Space for Control Flow ObfuscationComputers & Security10.1016/j.cose.2024.103704(103704)Online publication date: Jan-2024
https://doi.org/10.1016/j.cose.2024.103704
Al-Kaswan AAhmed TIzadi MSawant ADevanbu Pvan Deursen A(2023)Extending Source Code Pre-Trained Language Models to Summarise Decompiled Binaries2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER56733.2023.00033(260-271)Online publication date: Mar-2023
https://doi.org/10.1109/SANER56733.2023.00033
Alaswad FEswaran P(2023)Investigating the superiority of Intel oneAPI IFX compiler on Intel CPUs using different optimization levels: A case study on a CFD system2023 4th IEEE Global Conference for Advancement in Technology (GCAT)10.1109/GCAT59970.2023.10353473(1-9)Online publication date: 6-Oct-2023
https://doi.org/10.1109/GCAT59970.2023.10353473
Zhu MHao D(2023)Compiler Auto-Tuning via Critical Flag Selection2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)10.1109/ASE56229.2023.00209(1000-1011)Online publication date: 11-Sep-2023
https://doi.org/10.1109/ASE56229.2023.00209
Abich GOst LReis RAbich GOst LReis R(2023)Early Soft Error Consistency AssessmentEarly Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Constrained IoT Edge Devices10.1007/978-3-031-18599-1_5(75-86)Online publication date: 1-Jan-2023
https://doi.org/10.1007/978-3-031-18599-1_5
Abich GOst LReis RAbich GOst LReis R(2023)Related WorksEarly Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Constrained IoT Edge Devices10.1007/978-3-031-18599-1_3(41-62)Online publication date: 1-Jan-2023
https://doi.org/10.1007/978-3-031-18599-1_3
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