research-article

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.

References

[1]

F. Agakov, E. Bonilla, J. Cavazos, B.Franke, G. Fursin, M. O'Boyle, J. Thomson, M. Toussaint, and C. Williams. Using machine learning to focus iterative optimization. In Proceedings of the International Symposium on Code Generation and Optimization (CGO), pages 295--305, Mar. 2006.

Digital Library

[2]

L. Almagor, K. D. Cooper, A. Grosul, T. J. Harvey, S. Reeves, D. Subramanian, L. Torczon, and T. Waterman. Compilation order matters: Exploring the structure of the space of compilation sequences using randomized search algorithms. In Proceedings of the ACM SIGPLAN Symposium on Languages, Compilers, and Tools for Embedded Systems (LCTES), pages 231--239, June 2004.

[3]

M. Arnold, S. Finka, D. Grove, M. Hind, and P. F. Sweeney. Adaptive optimization in the Jalapeno JVM. In Proceedings of the ACM SIGPLAN Conference on Object-Oriented Programming Systems, Languages, and Applications (OOPSLA), pages 47--65, Oct. 2000.

Digital Library

[4]

M. Arnold, M. Hind, and B. G. Ryder. Online feedback--directed optimization in Java. In Proceedings of the ACM SIGPLAN Conference on Object-Oriented Programming Systems, Languages, and Applications (OOPSLA), pages 111--129, Oct. 2002.

Digital Library

[5]

F. Bodin, T. Kisuki, P. Knijnenburg, M. O'Boyle, and E. Rohou. Iterative compilation in a non-linear optimisation space. In Proceedings of the Workshop on Profile and Feedback-Directed Compilation, in Conjunction with the Intl. Conf. on Parallel Architectures and Compilation Techniques (PACT), Oct. 1998.

[6]

J. Cavazos, G. Fursin, F. Agakov, E. Bonilla, M. F. P. O'Boyle, and O. Temam. Rapidly selecting good compiler optimizations using performance counters. In Proceedings of the International Symposium on Code Generation and Optimization (CGO), pages 185--197, Mar. 2007.

Digital Library

[7]

J. Cavazos and M. O'Boyle. Automatic tuning of inlining heuristics. In Proceedings of the Supercomputing Conference on High Performance Networking and Computing, Nov. 2005.

Digital Library

[8]

J. Cavazos and M. O'Boyle. Method-specific dynamic compilation using logistic regression. In Proceedings of the ACM SIGPLAN Conference on Object--Oriented Programming Systems, Languages, and Applications (OOPSLA), pages 229--240, Oct. 2006.

Digital Library

[9]

K. Chow and Y. Wu. Feedback-directed selection and characterization of compiler optimizations. In Proceedings of the Workshop on Feedback--Directed and Dynamic Optimization (FDDO), Nov. 1999.

[10]

K. D. Cooper, P. J. Schielke, and D. Subramanian. Optimizing for reduced code space using genetic algorithms. In Proceedings of the ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES), pages 1--9, May 1999.

Digital Library

[11]

K. Deb. Multi-Objective Optimization using Evolutionary Algorithms. Wiley, 2001.

Digital Library

[12]

G. Fursin, A. Cohen, M. O'Boyle, and O. Temam. Quick and practical run--time evaluation of multiple program optimizations. Transactions on High Performance Embedded Architectures and Compilation Techniques (HiPEAC), 1(1):13--31, 2006.

[13]

S. V. Gheorghita, H. Corporaal, and T. Basten. Iterative compilation for energy reduction. Journal of Embedded Computing}, 1(4):509--520, July 2005.

Digital Library

[14]

E. Granston and A. Holler. Automatic recommendation of compiler options. In Proceedings of the Workshop on Feedback-Directed and Dynamic Optimization (FDDO), Dec. 2001.

[15]

M. Haneda, P. M. W. Knijnenburg, and H. A. G. Wijshoff. Optimizing general purpose compiler optimization. In Proceedings of the International Conference on Computing Frontiers, pages 180---188, May 2005.

Digital Library

[16]

J. L. Henning. SPEC CPU2000: Measuring CPU performance in the new millennium. IEEE Computer, 33(7):28--35, July 2000.

Digital Library

[17]

K. Ishizaki, T. M., K. Kawachiya, T. Suganuma, O. Gohda, T. Inagaki, A. Koseki, K. Ogata, M. Kawahito, T. Yasue, T. Ogasawara, T. Onodera, H. Komatsu, and T. Nakatani. Effectiveness of cross-platform optimizations for a Java just-in-time compiler. In Proceedings of the ACM SIGPLAN Conference on Object--Oriented Programming Systems, Languages, and Applications (OOPSLA), pages 187--204, Oct. 2003.

Digital Library

[18]

P. Kulkarni, S. Hines, J. Hiser, D. Whalley, J. Davidson, and D. Jones. Fast searches for effective optimization phase sequences. In Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), pages 171--182, June 2004.

Digital Library

[19]

D. Maier, P. Ramarao, M. Stoodley, and V. Sundaresan. Experiences with multithreading and dynamic class loading in a Java just-in-time compiler. In Proceedings of the International Symposium on Code Generation and Optimization (CGO), pages 87--97, Mar. 2006.

Digital Library

[20]

Z. Pan and R. Eigenmann. Fast, automatic, procedure-level performance tuning. In Proceedings of the International Conference on Parallel Architectures and Compilation Techniques (PACT), pages 173--181, Sept. 2006.

Digital Library

[21]

T. Suganuma, T. Yasue, M. Kawahito, H. Komatsu, and T. Nakatani. Design and evaluation of dynamic optimizations for a Java just-in-time compiler. ACM Transactions on Programming Languages and Systems (TOPLAS), 27(4):732---785, July 2005.

Digital Library

[22]

S. Triantafyllis, M. Vachharajani, and D. I. August. Compiler optimization--space exploration. Journal of Instruction--level Parallelism, Jan. 2005. Accessible at http://www.jilp.org/vol7.

[23]

H. Wu, E. Park, M. Kaplarevic, and Y. Zhang. Dynamic optimization option search in GCC. In Proceedings of the GCC Developers Summit 2007, June 2007.

[24]

M. Zhao, B. R. Childers, and M. L. Soffa. Predicting the impact of optimizations for embedded systems. In Proceedings of the ACM SIGPLAN Symposium on Languages, Compilers, and Tools for Embedded Systems (LCTES), pages 1--11, June 2003.

Digital Library

[25]

E. Zitzler, M. Laumanns, and L. Thiele. SPEA2: Improving the strength pareto evolutionary algorithm. Technical Report TIK--Report 103, Swiss Federal Institute of Technology (ETH) Zurich, May 2001.

[26]

E. Zitzler and L. Thiele. Multiobjective evolutionary algorithms: A comparative case study and the strength perato approach. IEEE Transactions on Evolutionary Computation, 3(4):257--271, Nov. 1999.

Digital Library

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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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.

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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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