Show HN: Agentic CUDA Kernel Optimizer

Contents How it works Setup Run Examples Results and limits Workflow graph An agentic CUDA kernel optimizer that turns workload descriptions into GPU implementations through an automated cycle of code generation, correctness checks, benchmarking, and refinement. Powered by LangGraph, the agent explores kernel implementations and launch configurations, queries GPU properties, and can research NVIDIA documentation for optimization guidance and inspect Nsight Compute counters to inform its next experiment. Each experiment is recorded, and the fastest validated implementation is retained.

The model can change both kernel code and per-case launch configurations. A standalone C++ harness compiles kernels with NVRTC, launches them through the CUDA Driver API, and saves outputs. Python handles comparison and candidate selection.