AMBER: A Columnar Architecture for High-Performance Agent-Based Modeling in Python

TL;DR

AMBER is a Python framework that significantly accelerates agent-based modeling by using a columnar data structure, enabling large-scale simulations with high performance and efficiency.

Contribution

It introduces a novel columnar architecture for Python agent-based modeling that combines conventional abstractions with compiled column operations for speed.

Findings

AMBER outperforms existing Python frameworks in execution speed.

Achieves up to 1118x speedup over Mesa.

Faster than Julia-based Agents.jl on large SIR benchmark.

Abstract

Python is widely used for agent-based modelling because it is accessible and has a mature scientific ecosystem, but object-per-agent execution incurs interpreter overhead that restricts the population sizes feasible in interactive modelling, calibration, and parameter sweeps. This paper presents AMBER, a Python framework that stores agent state in a Polars-backed columnar table and exposes population operations through a compact view API. The framework preserves conventional model and agent abstractions while translating common population updates into compiled column operations; behaviours that do not vectorise remain expressible through a buffered object-oriented path. We evaluate AMBER on wealth transfer, random walk, and spatial SIR benchmarks against Mesa, AgentPy, SimPy, Melodie, Agents.jl, and AMBER's own loop path, using invariant checks to verify comparable model outputs before timing. Across the tested workloads, AMBER has the lowest execution time among Python-hosted implementations and achieves speedups of up to $1118\times$ over Mesa; on the largest SIR benchmark it is also faster than the Julia-based Agents.jl implementation.