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@ARTICLE{Ejarque:907436,
      author       = {Ejarque, Jorge and Badia, Rosa M. and Albertin, Loïc and
                      Aloisio, Giovanni and Baglione, Enrico and Becerra, Yolanda
                      and Boschert, Stefan and Berlin, Julian R. and D’Anca,
                      Alessandro and Elia, Donatello and Exertier, François and
                      Fiore, Sandro and Flich, José and Folch, Arnau and Gibbons,
                      Steven J. and Koldunov, Nikolay and Lordan, Francesc and
                      Lorito, Stefano and Løvholt, Finn and Macías, Jorge and
                      Marozzo, Fabrizio and Michelini, Alberto and
                      Monterrubio-Velasco, Marisol and Pienkowska, Marta and de la
                      Puente, Josep and Queralt, Anna and Quintana-Ortí, Enrique
                      S. and Rodríguez, Juan E. and Romano, Fabrizio and Rossi,
                      Riccardo and Rybicki, Jedrzej and Kupczyk, Miroslaw and
                      Selva, Jacopo and Talia, Domenico and Tonini, Roberto and
                      Trunfio, Paolo and Volpe, Manuela},
      title        = {{E}nabling dynamic and intelligent workflows for {HPC},
                      data analytics, and {AI} convergence},
      journal      = {Future generation computer systems},
      volume       = {134},
      issn         = {0167-739X},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2022-02034},
      pages        = {414-429},
      year         = {2022},
      abstract     = {The evolution of High-Performance Computing (HPC) platforms
                      enables the design and execution of progressively larger and
                      more complex workflow applications in these systems. The
                      complexity comes not only from the number of elements that
                      compose the workflows but also from the type of computations
                      they perform. While traditional HPC workflows target
                      simulations and modelling of physical phenomena, current
                      needs require in addition data analytics (DA) and artificial
                      intelligence (AI) tasks. However, the development of these
                      workflows is hampered by the lack of proper programming
                      models and environments that support the integration of HPC,
                      DA, and AI, as well as the lack of tools to easily deploy
                      and execute the workflows in HPC systems. To progress in
                      this direction, this paper presents use cases where complex
                      workflows are required and investigates the main issues to
                      be addressed for the HPC/DA/AI convergence. Based on this
                      study, the paper identifies the challenges of a new workflow
                      platform to manage complex workflows. Finally, it proposes a
                      development approach for such a workflow platform addressing
                      these challenges in two directions: first, by defining a
                      software stack that provides the functionalities to manage
                      these complex workflows; and second, by proposing the HPC
                      Workflow as a Service (HPCWaaS) paradigm, which leverages
                      the software stack to facilitate the reusability of complex
                      workflows in federated HPC infrastructures. Proposals
                      presented in this work are subject to study and development
                      as part of the EuroHPC eFlows4HPC project.},
      cin          = {JSC},
      ddc          = {004},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5112},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000808123100004},
      doi          = {10.1016/j.future.2022.04.014},
      url          = {https://juser.fz-juelich.de/record/907436},
}