Thursday June 30, 2020

G. Riley (UniMan, UK), G. Aloisio (CMCC, IT)

Opening and Welcome

Session 1 
Exascale hardware
(Chair: G. Riley)
Abstracts and Slides

T. Schulthess


A useful definition of exascale computing for weather and climate modelling

Slides (pdf)

J. Labarta


Future HPC systems made in Europe

Slides (pdf)

J.M. Denis ATOS/EPI (FR)

European Processor Initiative: the European approach for Exascale ages

Slides (pdf)

K. Koskik


LUMI: the EuroHPC pre-exascale system ofthe North

Slides (pdf)

E. Suarez

Jülich Supercomputing Centre (DE)

Towards a Modular Supercomputing Architecture for Exascale

Slides (pdf)

Lunch Break
Session 2
Programming models andhardware interplay
(Chair: C. Osuna)
Abstracts and Slides

J. Goodacre

UniMan (UK)

The Euroexa system architecture for exascale

Slides (pdf)

R. Ford

Science and Technology Facilities Council (UK)

Developing DSLs in ESiWACE2

Slides (pdf)

H. Köstler
University of Erlangen-Nuremberg (DE)

Whole program code generation for ocean simulation

Slides (pdf)

S. McIntosh-Smith

University of Bristol (UK)

Exascale programming models: beyond “MPI+X”

Slides (pdf)

D. Lezzi


Programming dynamic workflows in the Exascale Era

Slides (pdf)

I. Kavcic

Met Office (UK)

LFRic and PSyclone: Utilising DSLs for performance portability

Slides (pdf)

Coffee Break
Session 3
Machine Learning
(Chair: G. Aloisio)
Abstracts and Slides

P. Dueben


Machine learning for weather predictions at ECMWF

Slides (pdf)

T. Hoefler

ETH Zurüch (CH)

Deep Learning for Post-Processing Ensemble Weather Forecasts

Slides (pdf)

O. Dunbar
Caltech (USA)

Efficiently constraining parameter uncertainty in a General Circulation Model using targeted data

Slides (pdf)

P. Gentine

Columbia University (USA)

Hybrid modeling: best of both worlds?

Slides (pdf)

C. Monteleoni

Colorado University (USA)

Climate Informatics: Machine Learning for the Study of Climate Change

Slides (pdf)

N. Brenowitz

Vulcan Inc. (USA)

Machine-learning of moist physics parameterizations for a climate model using coarse-graining of global cloud-resolving model output

Slides (pdf)

Wrap up and closing session