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DTSTAMP:20260422T000612Z
LOCATION:205-207
DTSTART;TZID=America/Denver:20231117T083000
DTEND;TZID=America/Denver:20231117T100000
UID:submissions.supercomputing.org_SC23_sess198@linklings.com
SUMMARY:Understanding the Performance, Reproducibility, Validation, Portab
 ility, and Sustainability of Coupled HPC Simulation and Deep Learning Calc
 ulations
DESCRIPTION:Understanding the Performance, Reproducibility, Validation, Po
 rtability, and Sustainability of Coupled HPC Simulation and Deep Learning 
 Calculations\n\nRecent advances in deep learning (DL) for scientific compu
 ting have paved the way for a new type of integrated programming environme
 nt. This environment must support the seamless integration of simulation a
 pplications with deep learning frameworks using methods such as in-memory 
 coupling and inferen...\n\n\nAda Sedova (Oak Ridge National Laboratory (OR
 NL)), Andrew Shao (Hewlett Packard Enterprise (HPE)), Daniel Reed (Univers
 ity of Utah), Karthik Kashinath (NVIDIA Corporation), Wesley Brewer (Oak R
 idge National Laboratory (ORNL)), and Daniel Martinez-Gonzalez (NASA Ames 
 Research Center)\n\nTag: Artificial Intelligence/Machine Learning, Applica
 tions, Reproducibility\n\nRegistration Category: Tech Program Reg Pass, Wo
 rkshop Reg Pass, Tutorial Reg Pass, Exhibits Reg Pass\n\nSession Chair: Ad
 a Sedova (Oak Ridge National Laboratory (ORNL))
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