Enhancing Computational Efficiency in NetLogo: Best Practices for Running Large-Scale Agent-Based Models on AWS and Cloud Infrastructures
New arXiv paper details optimization strategies for running large-scale agent-based models (ABMs) in the cloud.
Deep Dive
Researchers Michael A. Duprey and Georgiy V. Bobashev published a comprehensive guide on arXiv for optimizing NetLogo, a popular platform for agent-based models (ABMs), on AWS and other cloud infrastructures. Their paper covers best practices in memory management, Java options, and AWS instance selection. By implementing these optimizations, they demonstrated a 32% reduction in computational costs and improved performance consistency using the classic wolf-sheep predation model for testing.
Why It Matters
Provides a concrete roadmap for researchers and data scientists to run complex simulations faster and cheaper in the cloud.