Ave: Guiding Agentic GPU Optimization Using Data-Flow Invariants
Ave, an agentic framework that uses data-flow invariants as compile-time guardrails for kernel optimization and evaluates Ave on AMD MI300X across GEMM, flash attention, and MoE, which together account for up to 90% of GPU time in LLM inference.
Hao-Hui Mai, Xiao-Yang Guo, Xiang-Yun Ding et al.
· Proceedings of the ACM SIGOP... · 1 citation