A Survey on Agent-based Simulation using Hardware Accelerators

TL;DR

This survey reviews various techniques for accelerating agent-based simulations on hardware like GPUs, CPUs, and FPGAs, highlighting current methods, challenges, and future research directions for automation and performance optimization.

Contribution

It provides the first systematic overview and categorization of hardware acceleration techniques for agent-based simulations, identifying research gaps and future automation opportunities.

Findings

Various hardware platforms enable significant performance gains.

Model partitioning on heterogeneous hardware remains manual.

Future research should focus on automating hardware mapping.

Abstract

Due to decelerating gains in single-core CPU performance, computationally expensive simulations are increasingly executed on highly parallel hardware platforms. Agent-based simulations, where simulated entities act with a certain degree of autonomy, frequently provide ample opportunities for parallelisation. Thus, a vast variety of approaches proposed in the literature demonstrated considerable performance gains using hardware platforms such as many-core CPUs and GPUs, merged CPU-GPU chips as well as FPGAs. Typically, a combination of techniques is required to achieve high performance for a given simulation model, putting substantial burden on modellers. To the best of our knowledge, no systematic overview of techniques for agent-based simulations on hardware accelerators has been given in the literature. To close this gap, we provide an overview and categorisation of the literature according to the applied techniques. Since at the current state of research, challenges such as the partitioning of a model for execution on heterogeneous hardware are still a largely manual process, we sketch directions for future research towards automating the hardware mapping and execution. This survey targets modellers seeking an overview of suitable hardware platforms and execution techniques for a specific simulation model, as well as methodology researchers interested in potential research gaps requiring further exploration.