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.

Enabling dynamic and intelligent workflows for HPC, data analytics, and AI convergence / Ejarque, Jorge; Badia, Rosa M.; Albertin, Lo??c; Aloisio, Giovanni; Baglione, Enrico; Becerra, Yolanda; Boschert, Stefan; Berlin, Julian R.; D???anca, Alessandro; Elia, Donatello; Exertier, Fran??ois; Fiore, Sandro Luigi; Flich, Jos??; Folch, Arnau; Gibbons, Steven J.; Koldunov, Nikolay; Lordan, Francesc; Lorito, Stefano; L??vholt, Finn; Mac??as, Jorge; Marozzo, Fabrizio; Michelini, Alberto; Monterrubio-Velasco, Marisol; Pienkowska, Marta; de la Puente, Josep; Queralt, Anna; Quintana-Ort??, Enrique S.; Rodr??guez, Juan E.; Romano, Fabrizio; Rossi, Riccardo; Rybicki, Jedrzej; Kupczyk, Miroslaw; Selva, Jacopo; Talia, Domenico; Tonini, Roberto; Trunfio, Paolo; Volpe, Manuela. - In: FUTURE GENERATION COMPUTER SYSTEMS. - ISSN 0167-739X. - 134:(2022), pp. 414-429. [10.1016/j.future.2022.04.014]

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

Sandro Fiore;
2022-01-01

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.
2022
Ejarque, Jorge; Badia, Rosa M.; Albertin, Lo??c; Aloisio, Giovanni; Baglione, Enrico; Becerra, Yolanda; Boschert, Stefan; Berlin, Julian R.; D???anca, Alessandro; Elia, Donatello; Exertier, Fran??ois; Fiore, Sandro Luigi; Flich, Jos??; Folch, Arnau; Gibbons, Steven J.; Koldunov, Nikolay; Lordan, Francesc; Lorito, Stefano; L??vholt, Finn; Mac??as, Jorge; Marozzo, Fabrizio; Michelini, Alberto; Monterrubio-Velasco, Marisol; Pienkowska, Marta; de la Puente, Josep; Queralt, Anna; Quintana-Ort??, Enrique S.; Rodr??guez, Juan E.; Romano, Fabrizio; Rossi, Riccardo; Rybicki, Jedrzej; Kupczyk, Miroslaw; Selva, Jacopo; Talia, Domenico; Tonini, Roberto; Trunfio, Paolo; Volpe, Manuela
Enabling dynamic and intelligent workflows for HPC, data analytics, and AI convergence / Ejarque, Jorge; Badia, Rosa M.; Albertin, Lo??c; Aloisio, Giovanni; Baglione, Enrico; Becerra, Yolanda; Boschert, Stefan; Berlin, Julian R.; D???anca, Alessandro; Elia, Donatello; Exertier, Fran??ois; Fiore, Sandro Luigi; Flich, Jos??; Folch, Arnau; Gibbons, Steven J.; Koldunov, Nikolay; Lordan, Francesc; Lorito, Stefano; L??vholt, Finn; Mac??as, Jorge; Marozzo, Fabrizio; Michelini, Alberto; Monterrubio-Velasco, Marisol; Pienkowska, Marta; de la Puente, Josep; Queralt, Anna; Quintana-Ort??, Enrique S.; Rodr??guez, Juan E.; Romano, Fabrizio; Rossi, Riccardo; Rybicki, Jedrzej; Kupczyk, Miroslaw; Selva, Jacopo; Talia, Domenico; Tonini, Roberto; Trunfio, Paolo; Volpe, Manuela. - In: FUTURE GENERATION COMPUTER SYSTEMS. - ISSN 0167-739X. - 134:(2022), pp. 414-429. [10.1016/j.future.2022.04.014]
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