  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.
