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PassNet uses LLMs to generate compiler passes for performance optimization

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced PassNet, a novel framework designed to leverage large language models (LLMs) for generating compiler passes, which are crucial for optimizing code performance. Existing tensor compilers struggle with long-tail workloads, often leading to performance degradation. PassNet aims to address this by enabling LLMs to author structured graph transformations that can be integrated into compiler pipelines. The system includes a large dataset of computational graphs and a benchmark suite to evaluate LLM performance in this domain. AI

IMPACT This research could significantly improve the performance of AI models on specialized hardware by enabling more efficient compilation of complex computational graphs.

RANK_REASON This is a research paper detailing a new method and dataset for using LLMs in compiler optimization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

PassNet uses LLMs to generate compiler passes for performance optimization

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This is a research paper detailing a new method and dataset for using LLMs in compiler optimization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yiqun Liu, Yingsheng Wu, Ruqi Yang, Enrong Zheng, Honglei Qiu, Sijun He, Tai Liang, Jingjing Wu, Yuhan Zhou, Yiwei Zhang, Dongyan Chen, Weihan Yi, Xinqi Li, Siqi Bao ·

    PassNet: Scaling Large Language Models for Graph Compiler Pass Generation

    arXiv:2605.29357v1 Announce Type: new Abstract: Modern tensor compilers such as TorchInductor deliver substantial speedups on mainstream models, yet face a systematic performance ceiling on long-tail workloads -- our profiling shows that 43% of real-world subgraphs experience end…

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