Enabling Dynamic and Intelligent Workflows for HPC, Data Analytics, and AI Convergence
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.
Cite
@article{arxiv.2204.09287,
title = {Enabling Dynamic and Intelligent Workflows for HPC, Data Analytics, and AI Convergence},
author = {Jorge Ejarque and Rosa M. Badia and Loïc Albertin and Giovanni Aloisio and Enrico Baglione and Yolanda Becerra and Stefan Boschert and Julian R. Berlin and Alessandro D'Anca and Donatello Elia and François Exertier and Sandro Fiore and José Flich and Arnau Folch and Steven J Gibbons and Nikolay Koldunov and Francesc Lordan and Stefano Lorito and Finn Løvholt and Jorge Macías and Fabrizio Marozzo and Alberto Michelini and Marisol Monterrubio-Velasco and Marta Pienkowska and Josep de la Puente and Anna Queralt and Enrique S. Quintana-Ortí and Juan E. Rodríguez and Fabrizio Romano and Riccardo Rossi and Jedrzej Rybicki and Miroslaw Kupczyk and Jacopo Selva and Domenico Talia and Roberto Tonini and Paolo Trunfio and Manuela Volp},
journal= {arXiv preprint arXiv:2204.09287},
year = {2022}
}