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DTSTAMP:20250822T115808Z
LOCATION:Room 5.2D11
DTSTART;TZID=Europe/Stockholm:20250617T150000
DTEND;TZID=Europe/Stockholm:20250617T170000
UID:submissions.pasc-conference.org_PASC25_sess141@linklings.com
SUMMARY:MS4G - Moving Towards a More Sustainable HPC Ecosystem: A Full Sta
 ck Approach
DESCRIPTION:Global challenges around climate change necessitate a move to 
 a greener society. However, mathematical simulations on supercomputers and
  the recent rise in popularity of AI consumes vast amounts of energy with 
 associated environmental impact. Indeed, there are estimates that computin
 g could consume up to 20% of the world’s electricity by the end of the dec
 ade. This is not only bad for the environment, because energy is a signifi
 cant contributing factor to the CO2 emissions of computing, but it also li
 mits the size of a supercomputer because very few locations can deliver th
 e necessary amount of energy. Moreover, recent studies predict that data c
 entres may account for up to 8% of global Carbon emissions by 2030, howeve
 r despite these alarming numbers, moving HPC towards net zero has not seen
  the same urgency as other sectors. Given the scale of the challenge, this
  requires a full stack approach, identifying and exploring opportunities t
 o reduce our carbon footprint across the entire ecosystem. The purpose of 
 this minisymposium is to highlight opportunities for improved sustainabili
 ty in key areas of the HPC ecosystem, explore how we can address these as 
 a community and how these areas can interoperate to deliver a step change 
 in sustainability.\n\nMoving Towards More Sustainable Data-Center Operatio
 n\n\nIn order to move towards more sustainable HPC operations, it is impor
 tant to understand and improve the power efficiency of data centers. In NC
 AR's Computational and Information Systems Lab (CISL) we have focussed hea
 vily on reducing waste energy and ultimately driving a more efficient mode
  of opera...\n\n\nThomas Hauser (NCAR)\n---------------------\nHarnessing 
 Machine Learning for Long-Term ENSO Predictability: Insights from Ocean-At
 mosphere Interactions and Implications for Sustainable Climate Prediction\
 n\nAs the demand for high-performance computing (HPC) in weather and clima
 te grows, integrating machine learning (ML) techniques offers a promising 
 pathway to enhance predictive skill while addressing sustainability. This 
 talk presents an ML-driven approach to identify sources of long-term predi
 ctabili...\n\n\nIoana Colfescu (National Center of Atmospheric Research, U
 niversity of St Andrews)\n---------------------\nExploring Dataflow Archit
 ectures for Improved Efficiency in Earth System Models\n\nEarth System Mod
 els are crucial for simulating environmental processes but demand signific
 ant computational resources and energy. In this project we explore the pot
 ential of dataflow architectures to enhance both computational and energy 
 efficiency of ESMs. We will primarily discuss the Cerebras Waf...\n\n\nJus
 ts Zarins (EPCC)\n---------------------\nPanel\n\nOur presenters will take
  part in a panel to discuss, with the audience, the themes raised in the s
 ession and highlight overarching challenges and how we might move towards 
 a solution\n\n\nNick Brown (EPCC)\n\nDomain: Climate, Weather, and Earth S
 ciences, Computational Methods and Applied Mathematics\n\nSession Chair: N
 ick Brown (EPCC)
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