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Advancing HPC skills by developing Large Language Model Retrieval Augmented Generation (LLM-RAG) systems

Workshop: 12th SC Workshop on Best Practices for HPC Training and Education

Authors: Julia Mullen (MIT Lincoln Laboratory); Sam Corey, Lauren Milechin, and Riya Tyagi (Massachusetts Institute of Technology (MIT)); and Daniel Burrill (MIT Lincoln Laboratory)

Abstract: Large Artificial Intelligence (AI) and generative large language models (LLM) are key computational drivers. For researchers developing new tools or incorporating LLMs into their processing pipeline, the scale of data and models require supercomputing resources which can only be met through cloud or High Performance Computing (HPC) architectures. Many of these researchers have deep experience with AI, LLMs, and their research area but are new to HPC concepts, challenges, tools, and practices. To assist this researcher community, the Research Facilitation Teams at MIT Office of Research Computing and Data (ORCD) and the MIT Lincoln Laboratory Supercomputing Center (LLSC) have developed tutorial materials to teach researchers how to build their own Retrieval Augmented Generation (RAG) workflows. This work details LLM-RAG implementation concerns on two different systems, the design decisions associated with developing the examples, deployment of the workshop training, and the feed- back received from the participants.

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