[lustre-devel] Survey request to help with LLM development for Lustre

[John Bent]

Dear Lustre Community Members,

 We hope this message finds you well.

We are a research team from FIU and LANL. Following up on my recent talk at
last month’s Lustre User Group (LUG) meeting (LINK
<https://www.depts.ttu.edu/hpcc/events/LUG24/slides/Day2/LUG_2024_Talk_09-TASSI_John_Bent_LUG24.pdf>),
we are reaching out to invite you to participate in a survey to evaluate
the accuracy of Large Language Models (LLMs) in answering questions about
Lustre. Your expertise and experience are crucial for assessing how well
LLMs compare to human experts within our community.

Here are the details of the survey:

   - Number of Questions: 10
   - Estimated Completion Time: 15 ~ 30 minutes
   - Link: https://forms.gle/MHEf2FBYTjyRioa16

Note: We very much appreciate your time and contribution! Although it would
be wonderful if all of you are able to answer all ten questions, we realize
that this is a very large request. If you are able to help, please try to
respond to at least 3 questions and feel free to select the option “I
prefer not to answer” for the others.

In  this work, our ultimate goal is to improve the ability of local LLMs to
help administrators, users, and developers of Lustre. We will publish the
anonymized results of this survey back to this mailing list and hope to
publish research results in peer-reviewed conferences as well as report
back on our progress at future LUGs. Additionally, our work with local LLMs
will be done in open source. Finally, we recognize that LLMs are evolving
rapidly so we plan to repeat this exercise with other systems such as Ceph
as well as repeating this exercise at some point in the future with Lustre.
We hope to present results at a future LUG. Although, no future LUG could
ever surpass the awesome one just organized by the great folks at Texas
Tech, we know that they are all pretty great. :)

Thank you for your time and dedication to the Lustre community. Your
insights are invaluable, and we eagerly await your input.

In addition to the survey, we welcome any and all feedback on this specific
exercise as well as our research in general.

Thanks,

Hohnpeng, John, Raju, and Yanzhao
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