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MAchinE Learning for Scalable meTeoROlogy and cliMate

Project description

DEENESFRITPL

Customised machine learning solutions for weather and climate models

With climate change being described as the greatest threat facing modern humans ever, it’s necessary to develop the tools needed to prepare for its potential future effects. Machine learning can help improve weather and climate modelling. With that in mind, the EU-funded MAELSTROM project aims to improve European computer architecture to help evaluate future climate impacts. Specifically, it will advance compute system designs for optimal application performance and energy efficiency, a software framework to optimise usability and training efficiency for machine learning at scale, and large-scale machine learning applications for the domain of weather and climate science. Customised compute systems will be designed that are optimised for application needs to strengthen Europe’s high-performance computing portfolio.

Objective

To develop Europe’s computer architecture of the future, MAELSTROM will co-design bespoke compute system designs for optimal application performance and energy efficiency, a software framework to optimise usability and training efficiency for machine learning at scale, and large-scale machine learning applications for the domain of weather and climate science.

The MAELSTROM compute system designs will benchmark the applications across a range of computing systems regarding energy consumption, time-to-solution, numerical precision and solution accuracy. Customised compute systems will be designed that are optimised for application needs to strengthen Europe’s high-performance computing portfolio and to pull recent hardware developments, driven by general machine learning applications, toward needs of weather and climate applications.

The MAELSTROM software framework will enable scientists to apply and compare machine learning tools and libraries efficiently across a wide range of computer systems. A user interface will link application developers with compute system designers, and automated benchmarking and error detection of machine learning solutions will be performed during the development phase. Tools will be published as open source.

The MAELSTROM machine learning applications will cover all important components of the workflow of weather and climate predictions including the processing of observations, the assimilation of observations to generate initial and reference conditions, model simulations, as well as post-processing of model data and the development of forecast products. For each application, benchmark datasets with up to 10 terabytes of data will be published online for training and machine learning tool-developments at the scale of the fastest supercomputers in the world. MAELSTROM machine learning solutions will serve as blueprint for a wide range of machine learning applications on supercomputers in the future.

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Programme(s)

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Call for proposal

H2020-JTI-EuroHPC-2019-1

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Funding Scheme

RIA - Research and Innovation action

Coordinator

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS
Net EU contribution
€ 380 625,00
Address
SHINFIELD PARK
RG2 9AX Reading
United Kingdom

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Region
South East (England) Berkshire, Buckinghamshire and Oxfordshire Berkshire
Activity type
Research Organisations
Links
Total cost
€ 761 250,00

Participants (6)

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Last update: 27 December 2023

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Permalink: https://cordis.europa.eu/project/id/955513

European Union, 2024