Ave: Guiding Agentic GPU Optimization Using Data-Flow Invariants
Quick summary
arXiv:2604.18616v2 Announce Type: replace-cross Abstract: 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.
Key takeaways
- arXiv:2604.18616v2 Announce Type: replace-cross Abstract: 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.
Why it matters
AI progress is not only a software story. Chips, data centers and energy decisions help determine which models can operate economically and what end users ultimately pay.
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