Agentic Orchestration of HPC Applications in Cloud
2026-07-03 • Distributed, Parallel, and Cluster Computing
Distributed, Parallel, and Cluster Computing
AI summaryⓘ
The authors explore how large language models (LLMs) can automate complex tasks in high-performance computing (HPC). They build intelligent agents that handle everything from packaging software into containers to running experiments on cloud systems using Kubernetes. Their approach was tested on four popular HPC applications and optimized for many cloud setups. The results show the agents can efficiently manage scaling and improve speed, following expert-validated methods. The authors also discuss best practices for designing these autonomous systems to work well with humans.
Large Language ModelsHigh-Performance ComputingKubernetesContainerizationCloud ComputingScaling StudyAutonomous AgentsOptimizationMulti-platform Images
Authors
Vanessa Sochat, Daniel Milroy
Abstract
Large Language Models (LLMs) are serving as a catalyst of change for research practices, touching the daily lives of staff scientists, software engineers, and system administrators. The developments promise new degrees of autonomy, where categories of human work and decision making are replaced by autonomous, goal-oriented systems. This transition necessitates novel architectural paradigms and solid understanding of the strengths and limitations of LLMs. In this work, we design agents to intelligently deliver the entire life-cycle of an HPC application experimental run in cloud -- creation and build of a container, deployment in Kubernetes, optimization, and orchestration of a scaling study. We pursue this task for four well-known HPC applications to build multi-platform images and optimize across 21 instance types in Kubernetes. We demonstrate successful linear scaling with patterns approved by human experts, designs that improve work time to completion, and review suggested best practices for agentic design and collaboration.