CelloAI: Leveraging Large Language Models for HPC Software Development in High Energy Physics

Next-generation High Energy Physics (HEP) experiments will generate unprecedented data volumes, necessitating High Performance Computing (HPC) integration alongside traditional high-throughput computing. However, HPC adoption in HEP is hindered by the challenge of porting legacy software to heterogeneous architectures and the sparse documentation of these complex scientific codebases. We present CelloAI, a locally hosted coding assistant that leverages Large Language Models with Retrieval-Augmented Generation to support High Energy Physics code documentation and generation. This local deployment ensures data privacy, eliminates recurring costs, and provides access to large context windows without external dependencies. CelloAI addresses code documentation and code generation through specialized components. For code documentation, the assistant provides: (a) Doxygen style comment generation by retrieving relevant information from text sources, (b) File-level summary generation, and (c) An interactive chatbot for code comprehension queries. For code generation, CelloAI employs syntax-aware chunking that preserve syntactic boundaries during embedding thus improving retrieval accuracy in large codebases. The system integrates callgraph knowledge to maintain dependency awareness during code modifications and provides AI-generated suggestions for performance optimization and accurate refactoring. Our results demonstrate that CelloAI can enhance code understanding and streamline certain development workflows, however domain expert oversight and validation is critical for reliable use of LLM-assistants in scientific computing contexts.