Multithreaded agent-based simulation

Goldsby, Michael E.; Pancerella, Carmen M.

doi:10.1109/wsc.2013.6721541

Cited by 10 publications

(16 citation statements)

References 10 publications

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“…Unfortunately, when agent density varies spatially over time, e.g. in flocking or grouping patterns, load balancing issues may occur [10,44,22]. A corner case of this issue is when simulating chemotaxis-like patterns, where agent movement is influenced by a chemical concentration gradient, which can result in millions of agents swarming to the same location [17].…”

Section: Introductionmentioning

confidence: 99%

“…While the majority of ABM toolkits [58,46,4,41] are targeted for singlethreaded execution on the CPU [48,22], there have been explicit attempts to parallelize some of them. Goldsby and Pancerella [22] describe adjustments made to the MASON agent-based simulation package [35] that allow the use of multiple threads without major changes to conventional agent-based programming. RepastJ [42] has adaptations to multi-core CPUs [15], while Repast Simphony [43] supports parallel execution at the scheduling mechanism level.…”

Section: Introductionmentioning

confidence: 99%

“…Goldsby and Pancerella [22] proposed to retrofit multithreading in the MA-SON ABM toolkit using an EP approach, where each thread is assigned an equal part of the simulation environment. Each simulation step is divided into two parts separated by a barrier that synchronizes access to shared environment data.…”

Section: Introductionmentioning

confidence: 99%

“…A multithreading approach allows BNSim to efficiently simulate large populations of bacteria. As in reference [22], simulation steps are divided in two parts with barrier synchronization, in a tick-tock pattern. During the "tick", agents perform their actions in parallel (AP).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Parallelization Strategies for Spatial Agent-Based Models

Fachada

Lopes

Martins

et al. 2016

Int J Parallel Prog

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Agent-based modeling (ABM) is a bottom-up modeling approach, where each entity of the system being modeled is uniquely represented as an independent decision-making agent. Large scale emergent behavior in ABMs is population sensitive. As such, the number of agents in a simulation should be able to reflect the reality of the system being modeled, which can be in the order of millions or billions of individuals in certain domains. A natural solution to reach acceptable scalability in commodity multi-core processors consists of decomposing models such that each component can be independently processed by a different thread in a concurrent manner. In this paper we present a multithreaded Java implementation of the PPHPC ABM, with two goals in mind: 1) compare the performance of this implementation with an existing NetLogo implementation; and, 2) study how different parallelization strategies impact simulation performance on a shared memory architecture. Results show that: 1) model parallelization can yield considerable performance gains; 2) distinct parallelization strategies offer specific trade-offs in terms of performance and simulation reproducibility; and, 3) PPHPC is a valid reference model for comparing distinct implementations or parallelization strategies, from both performance and statistical accuracy perspectives.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parallelization Strategies for Spatial Agent-Based Models

Fachada

Lopes

Martins

et al. 2016

Int J Parallel Prog

View full text Add to dashboard Cite

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“…However, its multiple versions and user-interface/visualization goals limit the series appeal as a pure computational model for the goals described in the introduction. Other paradigmatic models which have been recurrently used, studied and replicated include Sugarscape Axtell et al, 1996;Bigbee, Cioffi-Revilla & Luke, 2007;D'Souza, Lysenko & Rahmani, 2007;Lysenko & D'Souza, 2008), Heatbugs (Wilensky, 2004;Sallach & Mellarkod, 2005;Goldsby & Pancerella, 2013), Boids (Reynolds, 1987;Reynolds, 2006;Goldsby & Pancerella, 2013) and several interpretations of prototypical predator-prey models (Smith, 1991;Hiebeler, 1994;Wilensky, 1997;Tatara et al, 2006;Ottino-Loffler, Rand & Wilensky, 2007;Ginovart, 2014). Nonetheless, there is a lack of formalization and in-depth statistical analysis of simulation output in most of these implementations, often leading to model assessment and replication difficulties (Edmonds & Hales, 2003;Wilensky & Rand, 2007).…”

Section: Introductionmentioning

confidence: 99%

Towards a standard model for research in agent-based modeling and simulation

Fachada

Lopes

Martins

et al. 2015

PeerJ Computer Science

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Agent-based modeling (ABM) is a bottom-up modeling approach, where each entity of the system being modeled is uniquely represented as an independent decisionmaking agent. ABMs are very sensitive to implementation details. Thus, it is very easy to inadvertently introduce changes which modify model dynamics. Such problems usually arise due to the lack of transparency in model descriptions, which constrains how models are assessed, implemented and replicated. In this paper, we present PPHPC, a model which aims to serve as a standard in agent based modeling research, namely, but not limited to, conceptual model specification, statistical analysis of simulation output, model comparison and parallelization studies. This paper focuses on the first two aspects (conceptual model specification and statistical analysis of simulation output), also providing a canonical implementation of PPHPC. The paper serves as a complete reference to the presented model, and can be used as a tutorial for simulation practitioners who wish to improve the way they communicate their ABMs.

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A Formula-Driven Scalable Benchmark Model for ABM, Applied to FLAME GPU

Alzahrani

Richmond

Simons

2018

Euro-Par 2017: Parallel Processing Workshops

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Agent Based Modelling (ABM) systems have become a popular technique for describing complex and dynamic systems. ABM is the simulation of intelligent agents and how these agents communicate with each other within the model. The growing number of agent-based applications in the simulation and AI fields led to an increase in the number of studies that focused on evaluating modelling capabilities of these applications. Observing system performance and how applications behave during increases in population size is the main factor for benchmarking in most of these studies. System scalability is not the only issue that may affect the overall performance, but there are some issues that need to be dealt with to create a standard benchmark model that meets all ABM criteria. This paper presents a new benchmark model and benchmarks the performance characteristics of the FLAME GPU simulator as an example of a parallel framework for ABM. The aim of this model is to provide parameters to easily measure the following elements: system scalability, system homogeneity, and the ability to handle increases in the level of agent communications and model complexity. Results show that FLAME GPU demonstrates near linear scalability when increasing population size and when reducing homogeneity. The benchmark also shows a negative correlation between increasing the communication complexity between agents and execution time. The results create a baseline for improving the performance of FLAME GPU and allow the simulator to be contrasted with other multi-agent simulators.

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Multithreaded agent-based simulation

Cited by 10 publications

References 10 publications

Parallelization Strategies for Spatial Agent-Based Models

Parallelization Strategies for Spatial Agent-Based Models

Towards a standard model for research in agent-based modeling and simulation

A Formula-Driven Scalable Benchmark Model for ABM, Applied to FLAME GPU

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