# Twelve quick tips for designing AI-driven HPC workflows

arXiv: https://arxiv.org/abs/2606.07491 · HTML: https://mlsanity.com/paper/2606.07491/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 5 Jun 2026.

## Builds on (most cited first, 10 of 10)

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM](https://mlsanity.com/paper/2104.04473/) (2021)
- [Ray: A Distributed Framework for Emerging AI Applications](https://mlsanity.com/paper/1712.05889/) (2017)
- [Membership Inference Attacks on Machine Learning: A Survey](https://mlsanity.com/paper/2103.07853/) (2021)
- [Parsl: Pervasive Parallel Programming in Python](https://mlsanity.com/paper/1905.02158/) (2019)
- [DataStates-LLM: Lazy Asynchronous Checkpointing for Large Language Models](https://mlsanity.com/paper/2406.10707/) (2024)
- [Applying the FAIR Principles to computational workflows](https://mlsanity.com/paper/2410.03490/) (2024)
- [Enabling Dynamic and Intelligent Workflows for HPC, Data Analytics, and AI Convergence](https://mlsanity.com/paper/2204.09287/) (2022)
- [Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows](https://mlsanity.com/paper/2410.14943/) (2024)
- [Ten Simple Rules for Success with HPC, i.e. Responsibly BASHing that Linux Cluster](https://mlsanity.com/paper/2101.06737/) (2021)

## Abstract (arXiv)

High-performance computing (HPC) clusters remain the backbone of large-scale scientific computation, traditionally executing deterministic, linear pipelines optimised for predictable performance. However, the pervasive integration of artificial intelligence (AI) and foundation models into scientific research has introduced a fundamentally new computational paradigm. AI-driven workflows are characteristically iterative, data-driven, and probabilistic, introducing unique challenges regarding data gravity, heterogeneous resource management, and complex workflow orchestration. This guide provides twelve practical tips designed to help researchers design efficient, scalable, and reproducible AI-driven HPC workflows. By addressing critical system-level bottlenecks - such as containerisation for environment portability, strategic deployment of job arrays, explicit feedback loop mechanics, and I/O optimisation for small files - this article offers a framework for transitioning from rigid execution pipelines to adaptive, intelligent computational environments. While these architectural principles are broadly applicable across distributed environments, they are particularly tailored to the resource-intensive throughput demands of modern computational biology.