# Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions

arXiv: https://arxiv.org/abs/2505.08135 · HTML: https://mlsanity.com/paper/2505.08135/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 42 references that are in the map.
- First posted on arXiv 13 May 2025.

## Builds on (most cited first, 22 of 22)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents](https://mlsanity.com/paper/2306.03314/) (2023)
- [Automatic Generation of Programming Exercises and Code Explanations using Large Language Models](https://mlsanity.com/paper/2206.11861/) (2022)
- [A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges](https://mlsanity.com/paper/2303.17125/) (2023)
- [A Survey on LLM-based Code Generation for Low-Resource and Domain-Specific Programming Languages](https://mlsanity.com/paper/2410.03981/) (2024)
- [Can Large Language Models Write Parallel Code?](https://mlsanity.com/paper/2401.12554/) (2024)
- [Comparing Llama-2 and GPT-3 LLMs for HPC kernels generation](https://mlsanity.com/paper/2309.07103/) (2023)
- [AI-assisted Code Authoring at Scale: Fine-tuning, deploying, and mixed methods evaluation](https://mlsanity.com/paper/2305.12050/) (2023)
- [Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation](https://mlsanity.com/paper/2306.15121/) (2023)
- [The Landscape and Challenges of HPC Research and LLMs](https://mlsanity.com/paper/2402.02018/) (2024)
- [Performance-Aligned LLMs for Generating Fast Code](https://mlsanity.com/paper/2404.18864/) (2024)
- [CodeRosetta: Pushing the Boundaries of Unsupervised Code Translation for Parallel Programming](https://mlsanity.com/paper/2410.20527/) (2024)
- [LLM4VV: Developing LLM-Driven Testsuite for Compiler Validation](https://mlsanity.com/paper/2310.04963/) (2023)
- [Multi-line AI-assisted Code Authoring](https://mlsanity.com/paper/2402.04141/) (2024)
- [Reinventing High Performance Computing: Challenges and Opportunities](https://mlsanity.com/paper/2203.02544/) (2022)
- [Scalable Delivery of Scalable Libraries and Tools: How ECP Delivered a Software Ecosystem for Exascale and Beyond](https://mlsanity.com/paper/2311.06995/) (2023)
- [The State of Fortran](https://mlsanity.com/paper/2203.15110/) (2022)
- [A cast of thousands: How the IDEAS Productivity project has advanced software productivity and sustainability](https://mlsanity.com/paper/2311.02010/) (2023)
- [Towards Semantics Lifting for Scientific Computing: A Case Study on FFT](https://mlsanity.com/paper/2501.09201/) (2025)

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

- [AstraAI: LLMs, Retrieval, and AST-Guided Assistance for HPC Codebases](https://mlsanity.com/paper/2603.27423/) (2026)
- [Learning Reasoning World Models for Parallel Code](https://mlsanity.com/paper/2604.20926/) (2026)

## Abstract (arXiv)

We discuss the challenges and propose research directions for using AI to revolutionize the development of high-performance computing (HPC) software. AI technologies, in particular large language models, have transformed every aspect of software development. For its part, HPC software is recognized as a highly specialized scientific field of its own. We discuss the challenges associated with leveraging state-of-the-art AI technologies to develop such a unique and niche class of software and outline our research directions in the two US Department of Energy--funded projects for advancing HPC Software via AI: Ellora and Durban.