OpenRepair: An Automated Tool for OpenMP Data Race Repair via Retrieval-Augmented LLMs

OpenMP is a widely adopted shared-memory programming model, yet its concurrent execution is highly prone to data races, threatening software correctness and performance. Existing automated repair techniques depend heavily on manually designed synchronization strategies, which limit their applicability to diverse OpenMP programs. This paper demonstrates OpenRepair, an end-to-end automated tool designed to generate context-aware data-race fixes for OpenMP applications. OpenRepair features a robust pipeline that synergizes static analysis, retrieval-augmented generation (RAG), and large language models (LLMs). By automatically digesting runtime race reports, extracting program concurrency structures, and retrieving relevant repair knowledge, the tool seamlessly synthesizes precise, template-free patches.Evaluation on 97 OpenMP benchmarks and real-world applications highlights its scalability and practical effectiveness: OpenRepair successfully resolves 85 race conditions, improving repair capability by 44.1% over existing tools while introducing lower synchronization overhead.