AI 中文总结
该研究首次将LLMCompiler生成的程序嵌入应用于程序分析与优化任务，结合源与IR代码嵌入在算法分类上错误率1.54%、较SOTA提升12%，为代码优化提供新方向。
AI 中文摘要
近期研究进展凸显了机器学习，特别是大语言模型（LLMs）在程序分析与优化领域的应用潜力。我们首次将LLMCompiler（一种在中间表示（IR）代码上进行大规模预训练的大语言模型）生成的程序嵌入，应用于代表性的程序分析与优化任务。我们采用一种简单方法直接从源代码和IR代码生成程序嵌入：将程序拆分为多个块，用预训练的LLMs对每个块独立嵌入，再将块嵌入聚合为单个程序嵌入。实验表明，结合源代码与IR代码的嵌入，在算法分类任务中错误率达1.54%，较当前最优水平提升12%，在异构设备映射任务中也取得了有竞争力的准确率。这些发现表明，训练具备性能感知能力的大语言模型以嵌入IR代码，或可在代码优化任务中达到最优结果。
英文摘要
Recent advances have highlighted the potential of machine learning, particularly Large Language Models (LLMs), for analyzing and optimizing programs. We present the first application of program embeddings from LLMCompiler---an LLM massively pretrained on intermediate representation (IR) code---to representative program analysis and optimization tasks. We generate program embeddings directly from source and IR code using a simple approach: split programs into chunks, independently embed each chunk with pretrained LLMs, and then aggregate the chunk embeddings into a single program embedding. Our experiments show that combining source and IR code embeddings achieves an error rate of 1.54\% in algorithm classification, a 12\% improvement over the current state-of-the-art, and a competitive accuracy on heterogeneous device mapping. These findings suggest that training a performance-aware LLM for embedding IR code might yield state-of-the-art results in code optimization tasks.
Journal ref2025 International Joint Conference on Neural Networks (IJCNN), Rome, Italy, 2025, pp. 1-8
DOI：10.1109/IJCNN64981.2025.11227194
