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Computer Science > Programming Languages

arXiv:2309.07062 (cs)
[Submitted on 11 Sep 2023]

Title:Large Language Models for Compiler Optimization

Authors:Chris Cummins, Volker Seeker, Dejan Grubisic, Mostafa
Elhoushi, Youwei Liang, Baptiste Roziere, Jonas Gehring, Fabian
Gloeckle, Kim Hazelwood, Gabriel Synnaeve, Hugh Leather
Download a PDF of the paper titled Large Language Models for Compiler
Optimization, by Chris Cummins and 10 other authors
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    Abstract: We explore the novel application of Large Language
    Models to code optimization. We present a 7B-parameter
    transformer model trained from scratch to optimize LLVM assembly
    for code size. The model takes as input unoptimized assembly and
    outputs a list of compiler options to best optimize the program.
    Crucially, during training, we ask the model to predict the
    instruction counts before and after optimization, and the
    optimized code itself. These auxiliary learning tasks
    significantly improve the optimization performance of the model
    and improve the model's depth of understanding.
    We evaluate on a large suite of test programs. Our approach
    achieves a 3.0% improvement in reducing instruction counts over
    the compiler, outperforming two state-of-the-art baselines that
    require thousands of compilations. Furthermore, the model shows
    surprisingly strong code reasoning abilities, generating
    compilable code 91% of the time and perfectly emulating the
    output of the compiler 70% of the time.

          Programming Languages (cs.PL); Artificial Intelligence
Subjects: (cs.AI); Computation and Language (cs.CL); Machine Learning
          (cs.LG)
Cite as:  arXiv:2309.07062 [cs.PL]
          (or arXiv:2309.07062v1 [cs.PL] for this version)
          https://doi.org/10.48550/arXiv.2309.07062
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Chris Cummins [view email]
[v1] Mon, 11 Sep 2023 22:11:46 UTC (4,757 KB)
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