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DTSTAMP:20250822T115805Z
LOCATION:Campussaal - Plenary Room
DTSTART;TZID=Europe/Stockholm:20250617T103000
DTEND;TZID=Europe/Stockholm:20250617T110000
UID:submissions.pasc-conference.org_PASC25_sess150_pos113@linklings.com
SUMMARY:P29 - itwinai: Enabling Scalable AI Workflows on HPC for Digital T
 wins in Science
DESCRIPTION:Matteo Bunino, Anna Elisa Lappe, and Jarl Sondre Sæther (CERN)
 ; Rakesh Sarma (FZ Jülich); Maria Girone (CERN); and Andreas Lintermann (F
 orschungszentrum Jülich)\n\nThe interTwin project is advancing the integra
 tion of Digital Twins across scientific domains, focusing on physics and c
 limate research. A key component of this project is itwinai, a Python libr
 ary designed to streamline scalable AI workflows on High-Performance Compu
 ting (HPC) systems. With its unified interface, itwinai simplifies the dep
 loyment and optimisation of AI models across leading frameworks for distri
 buted machine learning. The library features tooling for profiling scalabi
 lity and monitoring GPU utilisation, allowing scientists to better underst
 and and show how well their code is distributed. It also helps to identify
  inefficiencies, enhancing sustainability and helping to develop greener A
 I solutions. Recent advancements include support for large-model paralleli
 sm and distributed hyperparameter optimization (HPO). By providing a unifo
 rm pipeline to run AI workflows easily and intuitively, itwinai lowers the
  barriers to these complex domains, empowering scientists to achieve repro
 ducible, high-performance results on HPC infrastructure. Through integrati
 on with interLink, itwinai facilitates seamless offloading of compute-inte
 nsive tasks from cloud to HPC. Validated on diverse use cases in physics a
 nd climate research, including collaborations with CMCC, EURAC and CERFACS
 , itwinai has shown that it has the potential to address challenges in ren
 ewable energy, climate modelling, and sustainable development.\n\nSession 
 Chair: David Moxey (King's College London)\n\n
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