xpillons discusses how Copilot Agents employ artificial intelligence to automate HPC job scripts for scientific computing, detailing iterative workflow enhancements and error reduction strategies.

Automating HPC Workflows with Copilot Agents

Let AI Do the Heavy Lifting

Introduction

High Performance Computing (HPC) workloads require precise scripting for job submissions and resource management. Manual approaches for platforms like OpenFOAM can be error-prone and time-consuming. At SC25, Copilot Agents were demonstrated as an AI-powered solution for automating Slurm submission scripts for scientific computing.

Why Automate HPC Workflows?

  • HPC workloads often need elaborate job submission scripts to best manage system resources.
  • Manual scripting is laborious and can introduce errors, causing job failures and research delays.
  • Automation speeds research, minimizes errors, and shifts focus from troubleshooting scripts to actionable simulation and analysis.

AI-powered Workflow Automation

Copilot Agents streamline scripting by using AI to:

  • Recognize workload context.
  • Apply best practices for script creation.
  • Generate precise Slurm scripts tailored to user requirements.
  • Ensure consistency and reduce mistakes in job submissions.

Typical Workflow with Copilot Agents

  1. Defining the Context
    • Specify workload requirements, application loading, node/task config, and any logging needs.
  2. Script Generation by AI
    • Copilot interprets instructions and creates Slurm job scripts, applying best scripting practices.
  3. Validation and Submission
    • Output scripts are validated then submitted; output and error logs are continuously reviewed for workflow improvement.

Best Practices for Defining Context

  • Give precise, thorough workload requirements.
  • Share relevant documentation and real-world usage examples.
  • Clearly state node/task needs, module loads, and logging.
  • Detailed context leads to better script quality and reduced errors.

Script Generation: Iterative Improvement

  • Model Selection: Advanced models (e.g., GPT-5) generate comprehensive scripts incorporating best practices and sophisticated options.
  • Iterative Development: Initial AI-generated scripts are refined through user and log feedback to match workload needs.
  • Example: Chat-based Copilot Agent creates Bash scripts using Slurm variables, manages module loading and distributes tasks, preparing jobs for sbatch submission.

Validation and Continuous Improvement

  • Review scripts before job execution to catch issues early.
  • Submit jobs for validation, monitor output and error logs.
  • Amend scripts in response to errors (e.g., updating file paths or module loads), leveraging AI feedback for fast corrections and reliable resubmissions.
  • Continuous iteration strengthens script dependability and workflow efficiency.

Key Benefits

  • Time Efficiency: Automates script creation, reducing manual effort from hours to minutes.
  • Error Reduction: Enforces best practices and standardization, minimizing human errors and failures.
  • Enhanced Scalability: Supports consistent automation across growing HPC environments.
  • User-Friendly Automation: Makes scripting accessible for less experienced users, with intuitive guidance and automation.

Version 1.0 - Updated December 3, 2025

Author: xpillons

Watch Copilot Agent Demo (OpenFOAM VSCode)

For further details or questions, visit xpillons’s profile or the Azure High Performance Computing Blog.

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