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

arXiv: https://arxiv.org/abs/2508.16713 · HTML: https://mlsanity.com/paper/2508.16713/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 35 references that are in the map.
- First posted on arXiv 22 Aug 2025.

## Builds on (most cited first, 17 of 17)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [StarCoder: may the source be with you!](https://mlsanity.com/paper/2305.06161/) (2023)
- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [LLMs Get Lost In Multi-Turn Conversation](https://mlsanity.com/paper/2505.06120/) (2025)
- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning](https://mlsanity.com/paper/2504.17192/) (2025)
- [Financial Report Chunking for Effective Retrieval Augmented Generation](https://mlsanity.com/paper/2402.05131/) (2024)
- [On the Effectiveness of Large Language Models in Domain-Specific Code Generation](https://mlsanity.com/paper/2312.01639/) (2023)
- [Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation](https://mlsanity.com/paper/2306.15121/) (2023)
- [HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights](https://mlsanity.com/paper/2505.04846/) (2025)
- [LASSI: An LLM-based Automated Self-Correcting Pipeline for Translating Parallel Scientific Codes](https://mlsanity.com/paper/2407.01638/) (2024)
- [Domain-specific ChatBots for Science using Embeddings](https://mlsanity.com/paper/2306.10067/) (2023)
- [Portable Programming Model Exploration for LArTPC Simulation in a Heterogeneous Computing Environment: OpenMP vs. SYCL](https://mlsanity.com/paper/2304.01841/) (2023)
- [Evaluating Portable Parallelization Strategies for Heterogeneous Architectures in High Energy Physics](https://mlsanity.com/paper/2306.15869/) (2023)
- [Packaging HEP Heterogeneous Mini-apps for Portable Benchmarking and Facility Evaluation on Modern HPCs](https://mlsanity.com/paper/2505.08933/) (2025)

## Built on by (most cited first, 2 of 2)

- [The State of Generative AI in Software Development: Insights from Literature and a Developer Survey](https://mlsanity.com/paper/2603.16975/) (2026)
- [Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy](https://mlsanity.com/paper/2607.21677/) (2026)

## Abstract (arXiv)

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.