The Advanced Scientific Computing (ASC) division carries out R&D activities on Computational Science applied to the Climate Change domain.
In particular, it focuses on:
- the development of advanced computing techniques and innovative algorithms for an optimal exploitation of numerical models on HPC architectures (High End Computing – HEC);
- the analysis and mining of large volumes of scientific data and management of analytical workflows looking forward to exascale scenarios (Data Science – DS);
- the exploration of Artificial Intelligence and Machine Learning methods on (pre) exascale environments in the climate change domain (Exascale Machine Learning for Climate Change – EMLC2);
- research on innovative digital platforms and tools for the delivery of new services in different sectors, such as agriculture, climate, disaster risk reduction, oceanography, water management, etc. (Production Platforms for Operational Services – PPOS).
Objectives
- Analysis, optimization and parallelization of numerical models on multi-cores hybrid and exascale computing architectures for climate change simulations (both climate and impacts models);
- Design and implementation of open source Data Science solutions addressing efficient access, analysis and mining of scientific data and user centered scientific workflow management in the climate change domain;
- Investigation, design and development of Machine/Deep Learning techniques to exploit pre-exascale and exascale HPC architectures in the climate change research context;
- Design and development of innovative digital platforms and tools, based on the integration of state-of-the-art and cutting-edge ICT technologies.
ASC Projects
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EOSC Beyond is a 36-month project coordinated by the EGI Foundation with…
ASC Publications
Bringing it all together: Science and modelling priorities to support international climate policy
Jones C. G.; Adloff F.; Booth B.; Cox P.; Eyring V.; Friedlingstein P.; Frieler K.; Hewitt H.; Jeffery H.; Joussaume S.; Koenigk T.; Lawrence B. N.; O'Rourke E.; Roberts M.; Sanderson B.; Séférian R.; Somot S.; Vidale P.-L.; van Vuuren D.; Acosta M.; Bentsen M.; Bernardello R.; Betts R.; Blockley E.; Boé J.; Bracegirdle T.; Braconnot P.; Brovkin V.; Buontempo C.; Doblas-Reyes F. J.; Donat M. G.; Epicoco I., Falloon P.; Fiore S.; Froelicher T.; Fuckar N.; Gidden M.; Goessling H.; Graversen R. G.; Gualdi S., Gutiérrez J. M.; Ilyina T.; Jacob D.; Jones C.; Juckes M.; Kendon E.; Kjellström E.; Knutti R.; Lowe J. A.; Mizielinski M.; Nassisi P., Obersteiner M.; Regnier P.; Roehrig R.; Salas y Melia D.; Schleussner C.-F.; Schulz M.; Scoccimarro E., Terray L.; Thiemann H.; Wood R.; Yang S.; and Zaehle, S.
2024, EGUsphere, doi: 10.5194/egusphere-2024-453
The computational and energy cost of simulation and storage for climate science: lessons from CMIP6
Acosta M.C.; Palomas S.; Paronuzzi Ticco S.V.; Utrera G.; Biercamp J.; Bretonniere P.-A.; Budich R.; Castrillo M.; Caubel A.; Doblas-Reyes F.; Epicoco I., Fladrich U.; Joussaume S.; Kumar Gupta A.; Lawrence B.; Le Sager P.; Lister G.; Moine M.-P.; Rioual J.-C.; Valcke S.; Zadeh N.; Balaji V.
2024, Geoscientific Model Development, Volume 17, Issue 8, Pages 3081 - 3098, doi: 10.5194/gmd-17-3081-2024
Contacts
Via Marco Biagi 5 – 73100 LECCE, Italy
[email protected]
0832 1902411
Research Units
Research Unit Leader
Italo Epicoco
The objectives of this research unit regard the analysis and optimization on “multi-cores” hybrid architectures of the main computational kernels featured in the models used at CMCC, by considering the performance impacts of optimized compilers, numerical libraries and new parallel paradigms. In addition, the activities also focus on the study of the impact of exascale computing architectures on the numerical algorithms used in the main climate models studied at CMCC. In particular, the following aspects are analyzed in exascale terms: (i) the use of advanced parallel algorithmic structures to reduce the current “dynamical cores” communication overhead; (ii) the optimal management of “multi-model” and “multi-emission” ensemble experiments; (iii) the optimal management of the memory system and its hierarchies; (iv) the rationalization of I/O operations; (v) the adoption of new communication parallel paradigms and parallel tasks synchronization mechanisms.
Research Unit Leader
Donatello Elia
The main goal of this Research Unit concerns the design and implementation of Data Science open source solutions addressing efficient access, analysis and mining of scientific data in the climate change domain. In particular, the activities focus on (i) the management of scientific data in major international contexts/initiatives like the ENES Climate Data Infrastructure, the Earth System Grid Federation, and the European Open Science Cloud, (ii) the definition of new storage models to enable efficient access to climate data (including parallel I/O approaches), and (iii) the development of advanced Data Science environments for climate scientists leveraging High Performance Data Analytics solutions as well as machine/deep learning frameworks to accelerate scientific discovery, (iv) the development of fault tolerant workflow automation tools and applications for the optimized scheduling of large number of tasks on HPC infrastructures designed to support weather and climate use cases.
Research Unit Leader
Gabriele Accarino
The objective of the EMLC2 Research Unit concerns the exploration and development of cutting-edge Machine/Deep Learning techniques and related applications in the context of climate science. The activities of this research unit are strictly connected to the availability of both huge amounts of data produced by the simulations and the increasing computational power of the forthcoming Exascale architectures. Specifically, the activities of this research unit focus on (i) the investigation of hybrid approaches, where critical computing Kernels of climate models are replaced with Neural Network algorithms without affecting results accuracy, (ii) the application of Machine/Deep Learning techniques for downscaling activities, by also exploring and comparing the application of different Neural Networks approaches (e.g. CNN or GAN), (iii) the analysis of the predictive capabilities of the Neural Networks (e.g. LSTM) for time series predictions in different weather and climate use cases, (iv) exploration of the application of ML/DL techniques in various scientific use cases: Tropical Cyclones, Conflicts Prediction, Monitoring & Processing of agroforestry parameters, study of Melting Glaciers and Analysis of Mars radargrams.
OPHIDIA
High Performance Data Mining & Analytics for eScience
Ophidia is a CMCC Foundation research project addressing big data challenges for eScience. It provides support for data-intensive analysis exploiting advanced parallel computing techniques and smart data distribution methods. It exploits an array-based storage model and a hierarchical storage organisation to partition and distribute multidimensional scientific datasets over multiple nodes. The Ophidia analytics framework can be exploited in different scientific domains (e.g. Climate Change, Earth Sciences, Life Sciences) and with very heterogeneous sets of data