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Enhancing Computational Efficiency in NetLogo: Best Practices for Running Large-Scale Agent-Based Models on AWS and Cloud Infrastructures

Michael A. Duprey, Georgiy V. Bobashev

TL;DR

This manuscript provides a comprehensive guide to optimizing NetLogo, a widely used platform for ABMs, for running large-scale models on Amazon Web Services (AWS) and other cloud infrastructures, and demonstrates the performance gains achievable through these optimizations.

Abstract

The rising complexity and scale of agent-based models (ABMs) necessitate efficient computational strategies to manage the increasing demand for processing power and memory. This manuscript provides a comprehensive guide to optimizing NetLogo, a widely used platform for ABMs, for running large-scale models on Amazon Web Services (AWS) and other cloud infrastructures. It covers best practices in memory management, Java options, BehaviorSpace execution, and AWS instance selection. By implementing these optimizations and selecting appropriate AWS instances, we achieved a 32\% reduction in computational costs and improved performance consistency. Through a comparative analysis of NetLogo simulations on different AWS instances using the wolf-sheep predation model, we demonstrate the performance gains achievable through these optimizations.

Enhancing Computational Efficiency in NetLogo: Best Practices for Running Large-Scale Agent-Based Models on AWS and Cloud Infrastructures

TL;DR

This manuscript provides a comprehensive guide to optimizing NetLogo, a widely used platform for ABMs, for running large-scale models on Amazon Web Services (AWS) and other cloud infrastructures, and demonstrates the performance gains achievable through these optimizations.

Abstract

The rising complexity and scale of agent-based models (ABMs) necessitate efficient computational strategies to manage the increasing demand for processing power and memory. This manuscript provides a comprehensive guide to optimizing NetLogo, a widely used platform for ABMs, for running large-scale models on Amazon Web Services (AWS) and other cloud infrastructures. It covers best practices in memory management, Java options, BehaviorSpace execution, and AWS instance selection. By implementing these optimizations and selecting appropriate AWS instances, we achieved a 32\% reduction in computational costs and improved performance consistency. Through a comparative analysis of NetLogo simulations on different AWS instances using the wolf-sheep predation model, we demonstrate the performance gains achievable through these optimizations.
Paper Structure (33 sections, 1 figure, 7 tables)

This paper contains 33 sections, 1 figure, 7 tables.

Table of Contents

  1. Introduction
  2. Optimizing NetLogo
  3. Use of NetLogo 6.4.0+
  4. Optimizing Java Options for NetLogo
  5. Memory Allocation
  6. Garbage Collection Optimization
  7. Other JVM Options
  8. Optimizing BehaviorSpace
  9. Parallel Execution of Simulations
  10. Efficient Data Recording
  11. Repetitions
  12. Random Seed Control
  13. Running BehaviorSpace Headlessly
  14. Choosing the Right AWS Instance
  15. Understanding AWS Instance Types
  16. ...and 18 more sections

Figures (1)

  • Figure 1: Comparison of AWS instance performance and cost-efficiency metrics