Url https://www.cimne.com/sgp/rtd/Project.aspx?id=892
LogoEntFinanc LogoPrograma
Acronym eFlows4HPC
Project title Enabling dynamic and Intelligent workflows in the future EuroHPCecosystem
Reference 955558
Principal investigator Riccardo ROSSI - rrossi@cimne.upc.edu
Start date 01/01/2021 End date 31/12/2023
Coordinator BSC
Consortium members
  • INRIA
  • NGI
  • CIMNE
  • FZJ
  • DtoK Lab S.r.l.
  • FONDAZIONE CMCC
  • PSNC
  • INGV
  • AWI
  • BULL
  • ETHZ
  • UPV
  • UNIVERSIDAD DE MALAGA
  • SIEMENS
  • SCUOLA INTERNAZIONALE SUPERIORE DI STUDI AVANZATI DI TRIESTE
Program H2020 (2014-2020) Call H2020-JTI-EuroHPC-2019-1
Subprogram EuroHPC- Joint Undertaking Category Europeo
Funding body(ies) EC Grant 245.164,78 €
Abstract Today, developers lack tools that enable the development of complex workflows involving HPC simulation and modelling with data analytics (DA) and machine learning (ML). TheFlows4HPC aims to deliver a workflow software stack and an additional set of services to enable the integration of HPC simulation and modelling with big data analytics and machine learning in scientific and industrial applications. The software stack will allow to develop innovative adaptive workflows that efficiently use the computing resources and also considering innovative storage solutions. To widen the access to HPC to newcomers, the project will provide HPC Workflows as a Service (HPCWaaS), an environment for sharing, reusing, deploying and executing existing workflows on HPC systems. The workflow technologies, associated machine learning and big data libraries used in the project leverages previous open source European initiatives. Specific optimization tasks for the use of accelerators (FPGAs, GPUs) and the EPI will be performed in the project use cases. To demonstrate the workflow software stack, use cases from three thematic pillars have been selected. Pillar I focuses on the construction of DigitalTwins for the prototyping of complex manufactured objects integrating state-of-the-art adaptive solvers with machine learning and data-mining, contributing to the Industry 4.0 vision. Pillar II develops innovative adaptive workflows for climate and for the study of Tropical Cyclones (TC) in the context of the CMIP6 experiment, including in-situ analytics. Pillar III explores the modelling of natural catastrophes - in particular, earthquakes and their associated tsunamisshortly after such an event is recorded. Leveraging two existing workflows, the Pillar will work of integrating them with the eFlows4HPC software stack and on producing policies for urgent access to supercomputers. The pillar results will be demonstrated in the target community CoEs to foster adoption and get feedback.