arXiv:2605.29357v1 Announce Type: cross 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-to-end slowdowns under default compilation. While LLMs offer a path toward automated optimization, existing efforts focus on standalone kernel generation. We argue that pass generation -- where LLMs author structured graph transformations that integrate directly into compiler pipelines -- is the more appropriate abs
The increasing complexity of AI models and the critical need for performance optimization are driving researchers to leverage LLMs for compiler automation.
This development indicates a significant advancement in automating complex software engineering tasks, particularly in the performance-critical domain of AI model compilation and deployment.
LLMs are moving beyond simple code generation towards more sophisticated, structured transformations within critical system pipelines like compilers, potentially reducing manual optimization efforts.
- · AI model developers
- · Cloud infrastructure providers
- · Hardware manufacturers
- · Software engineering tools
- · Manual compiler optimization specialists
- · Companies with inefficient AI deployment
Increased efficiency and performance for AI models, especially long-tail workloads.
Reduced operational costs and faster iteration cycles for AI development and deployment.
Further consolidation of AI capabilities among those who can effectively leverage LLM-driven compilers, widening the competitive gap.
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Read at arXiv cs.LG