Ave: Guiding Agentic GPU Optimization Using Data-Flow Invariants
LLM coding agents can generate correct GPU kernels, but their performance still trails expert libraries. Reaching peak throughput requires coordinating low-level optimizations such as shared-memory staging, software pipelining, and instruction scheduling. Yet unit tests and profiles provide only sparse end-to-end feedback, making it difficult for agents to identify which global constraints an optimization violates. We present Ave, an agentic framework that uses data-flow invariants as compile-time guardrails for GPU kernel optimization. Ave provides a tile-based Pythonic DSL that exposes hardware instructions and compiler policies while abstracting complex memory layouts. Tag functions as...
| Authors | Haohui Mai, Xiaoyan Guo, Xiangyun Ding et al. |
|---|---|
| Primary category | cs.DC |
| Published | 16 April 2026 |
| Last updated at source | 14 September 2026 |
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