ROCm Migration Copilot: AI CUDA-to-HIP Agent

Millions of lines of production GPU code are locked inside CUDA — and as AI compute demand explodes, developers are looking for an escape hatch into AMD's open ROCm ecosystem. The blocker: porting CUDA to HIP by hand is slow, error-prone, and requires rare expertise in both platforms. ROCm Migration Copilot turns a multi-week manual port into an afternoon. Feed it a CUDA repository and it runs a five-stage agentic pipeline: (1) deep codebase analysis, building a kernel inventory and dependency graph with AST parsing; (2) AI-assisted porting that translates CUDA APIs, kernels, and memory management to HIP, intelligently handling the long tail that hipify-perl misses; (3) automated build and test on real AMD GPUs; (4) a performance-tuning advisor driven by Omniperf and Omnitrace insights; (5) a migration report with confidence scores for every change. The stack is AMD end to end — ROCm, HIP, and AMD Developer Cloud MI300X instances — powered by an agentic LLM core with tool calling, RAG over ROCm documentation, and self-healing fix loops, orchestrated in Python and LangGraph. Everything ships open source. This project is designed for the AI Academy Challenge's highest-weight XP categories head-on: built on AMD technology, a complete working project, open-sourced, with monthly milestone drops and public demos through December. The goal is not a demo — it is a developer tool the ROCm community can actually use.