open access

https://hdl.handle.net/2115/92940

A Cholesky QR type algorithm for computing tall-skinny QR factorization with column pivoting

英語

Computational efficiency

2024-07-08

IEEE

2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

63-75

2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS).27-31 May 2024.San Francisco, CA, USA

Innovative methods, new instruments, disruptive techniques, and groundbreaking technologies have led tosignificant leaps in scientific progress. The increasingly powerful Large Language Models (LLMs) releasedeach month already speed up research activities such as concept explanation, literature search, andsummarization. The transformative potential of AI in research activities, in particular foundation models,raises important questions about their performance in science activities, their potential application indifferent contexts, and their ethics. In this talk, I will introduce AuroraGPT, Argonne National Laboratory'seffort to explore the notion of AI research assistants. To illustrate the gap between existing LLMs and anideal AI research assistant, I will first share observations from using existing LLMs as early researchassistants in three parallel and distributed computing experiments with experts in scheduling, distributedprotocols, and PDE solvers. AuroraGPT is developed as an open foundation model trained specifically withscientific data to explore solutions toward the realization of effective AI research assistants. I will describethe activity, challenges, and progress of the different groups developing the key aspects of AuroraGPT. Iwill particularly focus on the task of conversational research assistant and discuss the evaluation of LLMs'scientific skills, their safety and trustworthiness, and the co-design of a scientific benchmark with domainexperts.

会議発表論文

Accepted Manuscript

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1530-2075