Enabling dynamic and intelligent workflows for HPC, data analytics, and AI convergence

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 th...

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Veröffentlicht in:	Future generation computer systems Jg. 134; S. 414 - 429
Hauptverfasser:	Ejarque, Jorge, Badia, Rosa M., Albertin, Loïc, Aloisio, Giovanni, Baglione, Enrico, Becerra, Yolanda, Boschert, Stefan, Berlin, Julian R., D’Anca, Alessandro, Elia, Donatello, Exertier, François, Fiore, Sandro, Flich, José, Folch, Arnau, Gibbons, Steven J., Koldunov, Nikolay, Lordan, Francesc, Lorito, Stefano, Løvholt, Finn, Macías, Jorge, Marozzo, Fabrizio, Michelini, Alberto, Monterrubio-Velasco, Marisol, Pienkowska, Marta, de la Puente, Josep, Queralt, Anna, Quintana-Ortí, Enrique S., Rodríguez, Juan E., Romano, Fabrizio, Rossi, Riccardo, Rybicki, Jedrzej, Kupczyk, Miroslaw, Selva, Jacopo, Talia, Domenico, Tonini, Roberto, Trunfio, Paolo, Volpe, Manuela
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.09.2022
Schlagworte:	Distributed computing High performance computing HPC-DA-AI convergence Parallel programming Workflow development Workflow orchestration Parallel programming HPC-DA-AI convergence High performance computing Workflow development Distributed computing Workflow orchestration
ISSN:	0167-739X, 1872-7115
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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. •Analysis of the HPC, Big Data and AI convergence in complex scientific workflows.•Description of use cases where complex workflows combining HPC, DA and AI are required.•Present challenges of delivering a new workflow platform to manage complex workflows.•Software stack architecture to manage these complex workflows;•HPC Workflow as a Service to facilitate the reusability of complex workflows in HPC.
AbstractList	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. •Analysis of the HPC, Big Data and AI convergence in complex scientific workflows.•Description of use cases where complex workflows combining HPC, DA and AI are required.•Present challenges of delivering a new workflow platform to manage complex workflows.•Software stack architecture to manage these complex workflows;•HPC Workflow as a Service to facilitate the reusability of complex workflows in HPC.
Author	Lordan, Francesc Fiore, Sandro Løvholt, Finn Rossi, Riccardo Ejarque, Jorge Gibbons, Steven J. Queralt, Anna Selva, Jacopo Kupczyk, Miroslaw Koldunov, Nikolay Michelini, Alberto Baglione, Enrico Talia, Domenico Aloisio, Giovanni Folch, Arnau Becerra, Yolanda Rodríguez, Juan E. Monterrubio-Velasco, Marisol Elia, Donatello Volpe, Manuela Exertier, François Berlin, Julian R. Boschert, Stefan Quintana-Ortí, Enrique S. Lorito, Stefano de la Puente, Josep Rybicki, Jedrzej Badia, Rosa M. Albertin, Loïc D’Anca, Alessandro Tonini, Roberto Trunfio, Paolo Romano, Fabrizio Macías, Jorge Flich, José Marozzo, Fabrizio Pienkowska, Marta
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SubjectTerms	Distributed computing High performance computing HPC-DA-AI convergence Parallel programming Workflow development Workflow orchestration
Title	Enabling dynamic and intelligent workflows for HPC, data analytics, and AI convergence
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