AI-coupled HPC Workflows

TL;DR

This paper explores the integration of AI/ML models into HPC workflows, highlighting various modes, challenges, solutions, and the significant scientific advancements enabled by their convergence.

Contribution

It provides a comprehensive overview of AI-coupled HPC workflows, including modes of integration, real-world use cases, challenges, and middleware solutions, emphasizing their impact on scientific discovery.

Findings

AI/ML integration reduces computational needs in HPC workflows.

Coupling AI/ML with HPC enhances scientific performance and enables new explorations.

Frameworks and middleware address challenges like heterogeneity and performance in AI-coupled HPC.

Abstract

Increasingly, scientific discovery requires sophisticated and scalable workflows. Workflows have become the ``new applications,'' wherein multi-scale computing campaigns comprise multiple and heterogeneous executable tasks. In particular, the introduction of AI/ML models into the traditional HPC workflows has been an enabler of highly accurate modeling, typically reducing computational needs compared to traditional methods. This chapter discusses various modes of integrating AI/ML models to HPC computations, resulting in diverse types of AI-coupled HPC workflows. The increasing need of coupling AI/ML and HPC across scientific domains is motivated, and then exemplified by a number of production-grade use cases for each mode. We additionally discuss the primary challenges of extreme-scale AI-coupled HPC campaigns -- task heterogeneity, adaptivity, performance -- and several framework and middleware solutions which aim to address them. While both HPC workflow and AI/ML computing paradigms are independently effective, we highlight how their integration, and ultimate convergence, is leading to significant improvements in scientific performance across a range of domains, ultimately resulting in scientific explorations otherwise unattainable.