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End-to-End Workflows for Climate Science: Integrating HPC Simulations, Big Data Processing, and Machine Learning

Authors :
Barcelona Supercomputing Center
Elia, Donatello
Scardigno, Sonia
Ejarque, Jorge
D'anca, Alessandro
Accarino, Gabriele
Scoccimarro, Enrico
Donno, Davide
Peano, Daniele
Immorlano, Francesco
Aloisio, Giovanni
Barcelona Supercomputing Center
Elia, Donatello
Scardigno, Sonia
Ejarque, Jorge
D'anca, Alessandro
Accarino, Gabriele
Scoccimarro, Enrico
Donno, Davide
Peano, Daniele
Immorlano, Francesco
Aloisio, Giovanni
Publication Year :
2023

Abstract

Current scientific workflow systems do not typically integrate simulation-centric and data-centric aspects due to their very different software/infrastructure requirements. A transparent integration of such components into a single end-to-end workflow would lead to a more efficient and automated way for generating insights from large simulation data. This work presents a complex case study related to extreme events analysis of future climate data that integrates in the same workflow numerical simulations, Big Data analytics and Machine Learning models. The case study is being implemented in the context of the eFlows4HPC project using the project’s software stack for deployment and orchestration of the workflow. The solution implemented in the project has shown to simplify the development and execution of end-to-end climate workflows with heterogeneous software requirements. Moreover, such an approach can, in the long term, increase the reuse of workflows by scientists and their portability over different HPC infrastructures.<br />Thiswork has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 955558. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Germany, France, Italy, Poland, Switzerland and Norway. In Spain, it has received complementary funding from MCIN/AEI/10.13039/501100011033, Spain and the European Union NextGenerationEU/PRTR (contracts PCI2021-121957, PCI2021-121931, PCI2021-121944, and PCI2021- 121927). In Italy, it has been preliminary approved for complimentary funding by Ministero dello Sviluppo Economico (MiSE) (ref. project prop. 2659). The authors also acknowledge financial support by MCIN/AEI /10.13039/501100011033, Spain through the "Severo Ochoa Programme for Centres of Excellence in R&D" under Grant CEX2021-001148-S, the Spanish Government (contract PID2019- 107255 GB) and by Generalitat de Catalunya (contract 2021-SGR-00412).<br />Peer Reviewed<br />Postprint (author's final draft)

Details

Database :
OAIster
Notes :
application/pdf, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1427145205
Document Type :
Electronic Resource