A locally hosted RAG-based coding assistant improves kernel retrieval and porting coverage for HEP codebases, though no tested LLM correctly ports the hardest kernels.
The Landscape and Challenges of HPC Research and LLMs
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abstract
Recently, language models (LMs), especially large language models (LLMs), have revolutionized the field of deep learning. Both encoder-decoder models and prompt-based techniques have shown immense potential for natural language processing and code-based tasks. Over the past several years, many research labs and institutions have invested heavily in high-performance computing, approaching or breaching exascale performance levels. In this paper, we posit that adapting and utilizing such language model-based techniques for tasks in high-performance computing (HPC) would be very beneficial. This study presents our reasoning behind the aforementioned position and highlights how existing ideas can be improved and adapted for HPC tasks.
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2025 1verdicts
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CelloAI: Leveraging Large Language Models for HPC Software Development in High Energy Physics
A locally hosted RAG-based coding assistant improves kernel retrieval and porting coverage for HEP codebases, though no tested LLM correctly ports the hardest kernels.