Kavli Affiliate: Salman Habib
| First 5 Authors: Mohammad Atif, Mohammad Atif, , ,
| Summary:
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 (LLMs) with retrieval-augmented generation
(RAG) to support HEP 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 two primary
use cases, code documentation and code generation, through specialized
components. For code documentation, the assistant provides: (a) Doxygen style
comment generation for all functions and classes by retrieving relevant
information from RAG sources (papers, posters, presentations), (b) file-level
summary generation, and (c) an interactive chatbot for code comprehension
queries. For code generation, CelloAI employs syntax-aware chunking strategies
that preserve syntactic boundaries during embedding, 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.
We evaluate CelloAI using real-world HEP applications from ATLAS, CMS, and DUNE
experiments, comparing different embedding models for code retrieval
effectiveness. Our results demonstrate the AI assistant’s capability to enhance
code understanding and support reliable code generation while maintaining the
transparency and safety requirements essential for scientific computing
environments.
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