• Paper: Improving Assembly Code Performance with Large Language Models v

    From John R Levine@21:1/5 to All on Mon May 19 12:54:22 2025
    They prompted some LLMs with C programs and the GCC -O3 assembly, with
    feedback when the result was faster and still correct. It seems to me
    like asking for trouble, but they claim they got 47% speedup and 96% still correct code. The paper ends with a contrived example where the LLM
    figured out that a C routine could be collapsed into a POPCNT
    instruction.


    Anjiang Wei, Tarun Suresh, Huanmi Tan, Yinglun Xu, Gagandeep Singh, Ke
    Wang, Alex Aiken

    Abstract

    Large language models (LLMs) have demonstrated strong performance across a
    wide range of programming tasks, yet their potential for code optimization remains underexplored. This work investigates whether LLMs can optimize
    the performance of assembly code, where fine-grained control over
    execution enables improvements that are difficult to express in high-level languages. We present a reinforcement learning framework that trains LLMs
    using Proximal Policy Optimization (PPO), guided by a reward function that considers both functional correctness, validated through test cases, and execution performance relative to the industry-standard compiler gcc -O3.
    To support this study, we introduce a benchmark of 8,072 real-world
    programs. Our model, Qwen2.5-Coder-7B-PPO, achieves 96.0% test pass rates
    and an average speedup of 1.47x over the gcc -O3 baseline, outperforming
    all 20 other models evaluated, including Claude-3.7-sonnet. These results indicate that reinforcement learning can unlock the potential of LLMs to
    serve as effective optimizers for assembly code performance.

    https://arxiv.org/abs/2505.11480

    Regards,
    John Levine, johnl@taugh.com, Taughannock Networks, Trumansburg NY
    Please consider the environment before reading this e-mail. https://jl.ly

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