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DISTRIBUTED MODEL EXPLORATION WITH EMEWS.

Nicholson Collier1,2, Justin M Wozniak2,3, Arindam Fadikar1,2

  • 1Decision and Infrastructure Sciences Division, Argonne National Laboratory, Lemont, IL, USA.

Proceedings of the ... Winter Simulation Conference. Winter Simulation Conference
|March 28, 2025
PubMed
Summary
This summary is machine-generated.

This tutorial introduces advancements to the Extreme-scale Model Exploration with Swift (EMEWS) framework for high-performance computing. New features enhance accessibility and workflow distribution for large-scale computational model analysis.

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Area of Science:

  • Computational Science
  • High-Performance Computing
  • Software Frameworks

Background:

  • Increasing availability of high-performance computing (HPC) resources enables new simulation and computational tool applications.
  • Model exploration (ME) frameworks are crucial for large-scale analyses like calibration and optimization of computational models.

Purpose of the Study:

  • Present recent advancements to the Extreme-scale Model Exploration with Swift (EMEWS) framework.
  • Highlight new capabilities for improved accessibility, distributed workflows, and project creation.

Main Methods:

  • Focus on three use-inspired EMEWS capabilities: binary installation, decoupled architecture (EMEWS DB) with task API, and enhanced project creation.
  • Demonstrate EMEWS DB connecting a Python Bayesian optimization algorithm to heterogeneous compute resources (local and remote).

Main Results:

  • Improved accessibility via binary installation.
  • New decoupled architecture (EMEWS DB) and task API enable distributed workflows on heterogeneous resources.
  • Enhanced capabilities for creating and managing EMEWS projects.

Conclusions:

  • The presented EMEWS advancements facilitate large-scale model exploration on HPC resources.
  • The worked example demonstrates the framework's utility in connecting diverse computational tools and resources.
  • Further details and code are publicly available, promoting broader adoption and research.