Descriptive Execution of HPC Applications and Workflows

2026-07-10

AI Summary

This work addresses the limitations of traditional high-performance computing (HPC), which relies on manual task scripting and scheduling and struggles to meet the automation demands of complex scientific workflows. The authors propose the first large language model–based autonomous agent framework that enables end-to-end automated execution of HPC workflows from descriptive instructions. The framework integrates Slurm/Flux job schedulers, low-latency AWS cloud infrastructure, and event monitoring mechanisms to support task definition, optimization, and scheduling. Experimental results demonstrate that the system efficiently deploys scalable experiments, accurately translates job specifications—with only occasional deviations in processor affinity—and successfully reproduces an expert-level variant calling pipeline, achieving consistent results in 18 out of 19 runs. These findings validate the framework’s feasibility and effectiveness in real-world HPC environments.

Abstract

The means to execute and orchestrate software components has changed from human-written code to descriptive prose. In high performance computing, this transition is represented in application orchestration, workload management, and system monitoring and debugging, to name a few. The underlying means to enable descriptive definition of tasks is the use of the Large Language Model with associated tool functions and resources. A combination of a model with access to such resources, modeled in software, encompasses an autonomous framework. As fully automated and agentic frameworks are developed for science, it is important to assess reliability and strategies scoped to specific tasks. In this work, we assess the extent to which an agentic framework can optimize and run an HPC scaling study with a low latency network in Amazon Web Services, accurately transform HPC job specifications between workload managers, and design and run an entire biosciences workflow. We find that the framework completes all three tasks while surfacing task-specific failure modes. In the scaling study, agents deploy and optimize applications but monitor running jobs inefficiently, preferring conservative fixed waits over event subscriptions. In job translation, they convert specifications between Slurm and Flux with high accuracy, with processor-affinity flags the most common error. In the bioscience workflow, the agent reproduces an expert-written variant-calling pipeline almost exactly -- agreeing with the reference call set in 18 of 19 completed runs -- and reaches this result through many distinct yet functionally equivalent workflow implementations. This information is invaluable moving forward to developing multi-cluster setups with scheduling and transformation handled by agents.

Problem

• High Performance Computing
• Autonomous Agents
• Large Language Models
• Workflow Orchestration
• Job Specification Translation

Innovation

• Large Language Model
• Agentic Framework
• Descriptive Execution
• HPC Workflow Orchestration
• Job Specification Translation