# Enabling Dynamic and Intelligent Workflows for HPC, Data Analytics, and AI Convergence

arXiv: https://arxiv.org/abs/2204.09287 · HTML: https://mlsanity.com/paper/2204.09287/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2022.

## Built on by (most cited first, 8 of 8)

- [Employing Artificial Intelligence to Steer Exascale Workflows with Colmena](https://mlsanity.com/paper/2408.14434/) (2024)
- [Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources](https://mlsanity.com/paper/2303.08803/) (2023)
- [An Ensemble Machine Learning Approach for Tropical Cyclone Detection Using ERA5 Reanalysis Data](https://mlsanity.com/paper/2306.07291/) (2023)
- [Scalable Runtime Architecture for Data-driven, Hybrid HPC and ML Workflow Applications](https://mlsanity.com/paper/2503.13343/) (2025)
- [Automated Dynamic AI Inference Scaling on HPC-Infrastructure: Integrating Kubernetes, Slurm and vLLM](https://mlsanity.com/paper/2511.21413/) (2025)
- [High-Performance Serverless Computing: A Systematic Literature Review on Serverless for HPC, AI, and Big Data](https://mlsanity.com/paper/2601.09334/) (2026)
- [A Workflow-Oriented Framework for Asynchronous Human-AI Collaboration in Hybrid and Compute-Intensive HPC Environments](https://mlsanity.com/paper/2605.03743/) (2026)
- [Twelve quick tips for designing AI-driven HPC workflows](https://mlsanity.com/paper/2606.07491/) (2026)

## Abstract (arXiv)

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.