# AI-coupled HPC Workflow Applications, Middleware and Performance

arXiv: https://arxiv.org/abs/2406.14315 · HTML: https://mlsanity.com/paper/2406.14315/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2024.

## Built on by (most cited first, 7 of 7)

- [The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science](https://mlsanity.com/paper/2509.09915/) (2025)
- [Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars](https://mlsanity.com/paper/2406.17812/) (2024)
- [Scalable Runtime Architecture for Data-driven, Hybrid HPC and ML Workflow Applications](https://mlsanity.com/paper/2503.13343/) (2025)
- [Intelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Training](https://mlsanity.com/paper/2508.03872/) (2025)
- [From Edge to HPC: Investigating Cross-Facility Data Streaming Architectures](https://mlsanity.com/paper/2509.24030/) (2025)
- [Adaptive Protein Design Protocols and Middleware](https://mlsanity.com/paper/2510.06396/) (2025)
- [Automated Dynamic AI Inference Scaling on HPC-Infrastructure: Integrating Kubernetes, Slurm and vLLM](https://mlsanity.com/paper/2511.21413/) (2025)

## Abstract (arXiv)

AI integration is revolutionizing the landscape of HPC simulations, enhancing the importance, use, and performance of AI-driven HPC workflows. This paper surveys the diverse and rapidly evolving field of AI-driven HPC and provides a common conceptual basis for understanding AI-driven HPC workflows. Specifically, we use insights from different modes of coupling AI into HPC workflows to propose six execution motifs most commonly found in scientific applications. The proposed set of execution motifs is by definition incomplete and evolving. However, they allow us to analyze the primary performance challenges underpinning AI-driven HPC workflows. We close with a listing of open challenges, research issues, and suggested areas of investigation including the the need for specific benchmarks that will help evaluate and improve the execution of AI-driven HPC workflows.