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Dataflow-based pruning for speeding up superoptimization

Authors:

Manasij Mukherjee,

John RegehrAuthors Info & Claims

Proceedings of the ACM on Programming Languages, Volume 4, Issue OOPSLA

Article No.: 177, Pages 1 - 24

https://doi.org/10.1145/3428245

Published: 13 November 2020 Publication History

Abstract

Superoptimization is a compilation strategy that uses search to improve code quality, rather than relying on a canned sequence of transformations, as traditional optimizing compilers do. This search can be seen as a program synthesis problem: from unoptimized code serving as a specification, the synthesis procedure attempts to create a more efficient implementation. An important family of synthesis algorithms works by enumerating candidates and then successively checking if each refines the specification, using an SMT solver. The contribution of this paper is a pruning technique which reduces the enumerative search space using fast dataflow-based techniques to discard synthesis candidates that contain symbolic constants and uninstantiated instructions. We demonstrate the effectiveness of this technique by improving the runtime of an enumerative synthesis procedure in the Souper superoptimizer for the LLVM intermediate representation. The techniques presented in this paper eliminate 65% of the solver calls made by Souper, making it 2.32x faster (14.54 hours vs 33.76 hours baseline, on a large multicore) at solving all 269,113 synthesis problems that Souper encounters when optimizing the C and C++ programs from SPEC CPU 2017.

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Superoptimization is a compilation strategy that uses search to improve code quality, rather than relying on a canned sequence of transformations, as traditional optimizing compilers do. This search can be seen as a program synthesis problem: from unoptimized code serving as a specification, the synthesis procedure attempts to create a more efficient implementation. An important family of synthesis algorithms works by enumerating candidates and then successively checking if each refines the specification, using an SMT solver. The contribution of this paper is a pruning technique which reduces the enumerative search space using fast dataflow-based techniques to discard synthesis candidates that contain symbolic constants and uninstantiated instructions. We demonstrate the effectiveness of this technique by improving the runtime of an enumerative synthesis procedure in the Souper superoptimizer for the LLVM intermediate representation.

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Cited By

Gao FChen HZhou YWang KFilkov VRay BZhou M(2024)Shoot Yourself in the Foot — Efficient Code Causes Inefficiency in Compiler OptimizationsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695548(1846-1857)Online publication date: 27-Oct-2024
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Index Terms

Dataflow-based pruning for speeding up superoptimization
1. Software and its engineering

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cover image Proceedings of the ACM on Programming Languages

Proceedings of the ACM on Programming Languages Volume 4, Issue OOPSLA

November 2020

3108 pages

EISSN:2475-1421

DOI:10.1145/3436718

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Copyright © 2020 Owner/Author.

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

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Association for Computing Machinery

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Publication History

Published: 13 November 2020

Published in PACMPL Volume 4, Issue OOPSLA

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Chen HFilkov VRay BZhou M(2024)Efficient Code Causes Inefficiency in Compiler OptimizationsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695369(2426-2428)Online publication date: 27-Oct-2024
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