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Jun Bi

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NeurIPS Conference 2025 Conference Paper

QiMeng-NeuComBack: Self-Evolving Translation from IR to Assembly Code

  • Hainan Fang
  • Yuanbo Wen
  • Jun Bi
  • Yihan Wang
  • Tonghui He
  • Yanlin Tang
  • Di Huang
  • Jiaming Guo

Compilers, while essential, are notoriously complex systems that demand prohibitively expensive human expertise to develop and maintain. The recent advancements in Large Language Models (LLMs) offer a compelling new paradigm: Neural Compilation, which could potentially simplify compiler development for new architectures and facilitate the discovery of innovative optimization techniques. However, several critical obstacles impede its practical adoption. Firstly, a significant lack of dedicated benchmarks and robust evaluation methodologies hinders objective assessment and tracking of progress in the field. Secondly, systematically enhancing the reliability and performance of LLM-generated assembly remains a critical challenge. Addressing these challenges, this paper introduces NeuComBack, a novel benchmark dataset specifically designed for IR-to-assembly compilation. Leveraging this dataset, we first define a foundational Neural Compilation workflow and conduct a comprehensive evaluation of the capabilities of recent frontier LLMs on Neural Compilation, establishing new performance baselines. We further propose a self-evolving prompt optimization method that enables LLMs to iteratively evolve their internal prompt strategies by extracting insights from prior self-debugging traces, thereby enhancing their neural compilation capabilities. Experiments demonstrate that our method significantly improves both the functional correctness and the performance of LLM-generated assembly code. Compared to baseline prompts, the functional correctness rates improved from 44% to 64% on x86 64 and from 36% to 58% on aarch64, respectively. More significantly, among the 16 correctly generated x86 64 programs using our method, 14 (87. 5%) surpassed clang-O3 performance. These consistent improvements across diverse architectures (x86_64 and aarch64) and program distributions (NeuComBack L1 and L2) validate our method's superiority over conventional approaches and its potential for broader adoption in low-level neural compilation.

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ICLR Conference 2023 Conference Paper

BALTO: fast tensor program optimization with diversity-based active learning

  • Jun Bi
  • Xiaqing Li
  • Qi Guo 0001
  • Rui Zhang 0040
  • Yuanbo Wen 0001
  • Xing Hu 0001
  • Zidong Du
  • Xinkai Song

Tensor program optimization (TPO) based on pre-trained models can effectively reduce the computing time of deep neural networks. However, training of such models is prohibitively expensive, which highly depends on a large-scale dataset and thus requires tremendous time-consuming performance measurements (more than 1 million) on target platforms. In this paper, we propose BALTO, a fast TPO approach with biased-diversity-based active learning, aiming at reducing much lower training costs under similar optimization accuracy.The key insight is that random sampling of existing approaches suffers from a heavy redundancy of low-performance programs, which incurs tremendous duplicated time-consuming measurements. Inspired by this, BALTO removes such redundancy by introducing active learning (AL) to TPO for a much lower training cost. However, applying AL with a brute-force way in BALTO can lead to an overestimation problem. To address this, we further propose a biased-diversity-based diversity scheme specially designed for BALTO. We compare BALTO against TenSet on $6$ typical hardware platforms over $2$ learning models. Experimental results show that, on average, BALTO only requires 5% of the total performance measurements of TenSet to achieve the same or higher model accuracy. Moreover, the optimized tensor programs even outperform that of TenSet by 1.06% due to higher model accuracy.

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