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Alive-Infer: data-driven precondition inference for peephole optimizations in LLVM

Authors:

David Menendez,

Santosh NagarakatteAuthors Info & Claims

PLDI 2017: Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Implementation

Pages 49 - 63

https://doi.org/10.1145/3062341.3062372

Published: 14 June 2017 Publication History

Abstract

Peephole optimizations are a common source of compiler bugs. Compiler developers typically transform an incorrect peephole optimization into a valid one by strengthening the precondition. This process is challenging and tedious. This paper proposes Alive-Infer, a data-driven approach that infers preconditions for peephole optimizations expressed in Alive. Alive-Infer generates positive and negative examples for an optimization, enumerates predicates on-demand, and learns a set of predicates that separate the positive and negative examples. Alive-Infer repeats this process until it finds a precondition that ensures the validity of the optimization. Alive-Infer reports both a weakest precondition and a set of succinct partial preconditions to the developer. Our prototype generates preconditions that are weaker than LLVM’s preconditions for 73 optimizations in the Alive suite. We also demonstrate the applicability of this technique to generalize 54 optimization patterns generated by Souper, an LLVM IR–based superoptimizer.

Supplementary Material

Auxiliary Archive (pldi17-main214-s.zip)

This artifact contains a complete distribution of Alive-Infer (previously known as "PInfer") distributed as a virtual machine compatible with Oracle's Virtual Box software. This environment includes the Z3 solver and Alive-Infer pre-built and configured for use, and several scripts for performing precondition inference, batch timing tests, and analysis of inferred preconditions. This artifact also includes a source distribution of Alive-Infer and Z3, ready to be built and run natively to avoid the performance penalty of running in a virtual environment.

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Nandi CWillsey MZhu AWang YSaiki BAnderson ASchulz AGrossman DTatlock Z(2021)Rewrite rule inference using equality saturationProceedings of the ACM on Programming Languages10.1145/34854965:OOPSLA(1-28)Online publication date: 15-Oct-2021
https://dl.acm.org/doi/10.1145/3485496
Xu QWong MWagle TNarayana SSivaraman AKuipers FCaesar M(2021)Synthesizing safe and efficient kernel extensions for packet processingProceedings of the 2021 ACM SIGCOMM 2021 Conference10.1145/3452296.3472929(50-64)Online publication date: 9-Aug-2021
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Newcomb JAdams AJohnson SBodik RKamil S(2020)Verifying and improving Halide’s term rewriting system with program synthesisProceedings of the ACM on Programming Languages10.1145/34282344:OOPSLA(1-28)Online publication date: 13-Nov-2020
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Index Terms

Alive-Infer: data-driven precondition inference for peephole optimizations in LLVM
1. Software and its engineering
  1. Software notations and tools
    1. Compilers
  2. Software organization and properties
    1. Software functional properties
      1. Formal methods
        Software verification

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PLDI 2017: Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Implementation

June 2017

708 pages

ISBN:9781450349888

DOI:10.1145/3062341

General Chair:
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Inria, France
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DeepCode, Switzerland / ETH Zurich, Switzerland

ACM SIGPLAN Notices Volume 52, Issue 6
PLDI '17
June 2017
708 pages
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EISSN:1558-1160
DOI:10.1145/3140587
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Cited By

Nandi CWillsey MZhu AWang YSaiki BAnderson ASchulz AGrossman DTatlock Z(2021)Rewrite rule inference using equality saturationProceedings of the ACM on Programming Languages10.1145/34854965:OOPSLA(1-28)Online publication date: 15-Oct-2021
https://dl.acm.org/doi/10.1145/3485496
Xu QWong MWagle TNarayana SSivaraman AKuipers FCaesar M(2021)Synthesizing safe and efficient kernel extensions for packet processingProceedings of the 2021 ACM SIGCOMM 2021 Conference10.1145/3452296.3472929(50-64)Online publication date: 9-Aug-2021
https://dl.acm.org/doi/10.1145/3452296.3472929
Newcomb JAdams AJohnson SBodik RKamil S(2020)Verifying and improving Halide’s term rewriting system with program synthesisProceedings of the ACM on Programming Languages10.1145/34282344:OOPSLA(1-28)Online publication date: 13-Nov-2020
https://dl.acm.org/doi/10.1145/3428234
Lim JNagarakatte SKandemir MJimborean AMoseley T(2019)Automatic equivalence checking for assembly implementations of cryptography librariesProceedings of the 2019 IEEE/ACM International Symposium on Code Generation and Optimization10.5555/3314872.3314880(37-49)Online publication date: 16-Feb-2019
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Lim JNagarakatte S(2019)Automatic Equivalence Checking for Assembly Implementations of Cryptography Libraries2019 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)10.1109/CGO.2019.8661180(37-49)Online publication date: Feb-2019
https://doi.org/10.1109/CGO.2019.8661180
Kang JKim YSong YLee JPark SShin MKim YCho SChoi JHur CYi K(2018)Crellvm: verified credible compilation for LLVMACM SIGPLAN Notices10.1145/3296979.319237753:4(631-645)Online publication date: 11-Jun-2018
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Kang JKim YSong YLee JPark SShin MKim YCho SChoi JHur CYi KFoster JGrossman D(2018)Crellvm: verified credible compilation for LLVMProceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation10.1145/3192366.3192377(631-645)Online publication date: 11-Jun-2018
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Ginsbach PCrawford LO'Boyle MDubach CXue J(2018)CAnDL: a domain specific language for compiler analysisProceedings of the 27th International Conference on Compiler Construction10.1145/3178372.3179515(151-162)Online publication date: 24-Feb-2018
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Lopes NMenendez DNagarakatte SRegehr J(2018)Practical verification of peephole optimizations with AliveCommunications of the ACM10.1145/316606461:2(84-91)Online publication date: 23-Jan-2018
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Mukherjee MRegehr J(2024)Hydra: Generalizing Peephole Optimizations with Program SynthesisProceedings of the ACM on Programming Languages10.1145/36498378:OOPSLA1(725-753)Online publication date: 29-Apr-2024
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