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UID:submissions.pasc-conference.org_PASC24_sess144_msa258@linklings.com
SUMMARY:Challenges and Opportunities in Combining LLMs with Conventional S
 imulation Workflows
DESCRIPTION:Minisymposium\n\nLogan Ward, Gautham Dharuman, Arvind Ramanath
 an, and Ian Foster (Argonne National Laboratory)\n\nThe effectiveness of A
 I for a variety of scientific tasks has rapidly improved over the last few
  years, changing the way that we can perform scientific workflows on HPC. 
 Our recent work deploying a workflow around a Large-Language Model (LLM) f
 or generating protein sequences is a good example of many of the opportuni
 ties and challenges. Embedding the LLM within a larger protein screening w
 orkflow enabled us to target simulations more effectively and find better 
 sequences faster; but was not easily accomplished with conventional workfl
 ow tools. Interleaving AI predictions required expressing dynamic actions 
 with the workflow application, the size of data being transferred around t
 he workflow required adding a secondary data transfer fabric, and the evol
 ving nature of the tasks being deployed on HPC required particular attenti
 on to caching elements of workflow tasks. We will discuss how we addressed
  these and other challenges in this presentation.\n\nDomain: Chemistry and
  Materials, Climate, Weather, and Earth Sciences, Engineering, Computation
 al Methods and Applied Mathematics\n\nSession Chair: Riccardo Balin (Argon
 ne National Laboratory)
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