AgentCompile: An LLM-Guided Compiler for Direct CUDA Inference
📰 ArXiv cs.AI
Learn how AgentCompile uses LLMs to optimize CUDA inference compilation and improve performance
Action Steps
- Build a compiler-derived region summary using AgentCompile
- Use LLM outputs as advisory search metadata to propose semantic decisions
- Configure the compiler to specialize regions and select CUDA implementation families
- Test the optimized compiler pipeline for improved performance
- Apply AgentCompile to existing inference workloads to measure performance gains
Who Needs to Know This
ML engineers and researchers working on optimizing inference pipelines can benefit from this technique to improve performance and efficiency
Key Insight
💡 LLMs can be used to guide compiler decisions and improve inference performance without requiring explicit model graph modifications
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🚀 AgentCompile: LLM-guided CUDA inference compiler for optimized performance! 🤖
Key Takeaways
Learn how AgentCompile uses LLMs to optimize CUDA inference compilation and improve performance
Full Article
Title: AgentCompile: An LLM-Guided Compiler for Direct CUDA Inference
Abstract:
arXiv:2606.07665v1 Announce Type: cross Abstract: Transformer inference increasingly depends on specialized compiler and runtime support, but real model graphs still require semantic decisions about which regions are worth specializing and which CUDA implementation families are plausible. We present AgentCompile, an LLM-guided CUDA inference compiler that uses LLM outputs only as advisory search metadata. Given compiler-derived region summaries and bounded candidate spaces, the LLM proposes sema
Abstract:
arXiv:2606.07665v1 Announce Type: cross Abstract: Transformer inference increasingly depends on specialized compiler and runtime support, but real model graphs still require semantic decisions about which regions are worth specializing and which CUDA implementation families are plausible. We present AgentCompile, an LLM-guided CUDA inference compiler that uses LLM outputs only as advisory search metadata. Given compiler-derived region summaries and bounded candidate spaces, the LLM proposes sema
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