Simulating Species Interactions and Complex Emergence in Multiple Flocks of Boids with GPUS

Husselmann, Alwyn V.; Hawick, K. A.

doi:10.2316/p.2011.757-012

Cited by 13 publications

(6 citation statements)

References 13 publications

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“…We decided against that as we believe that our results and models are more general (cross‐platform) if we develop them by means of classic, nonparallel, programming. Because both individual‐based models, and spatial partitioning are well suited for parallel approaches similar techniques could be used to greatly increase performance of the developed hybrid model.…”

Section: Resultsmentioning

confidence: 99%

“…To alleviate the issues that arise from the computational complexity and increase processing speed, several approaches have been suggested. Some researchers reduced the computational workload by decreasing the update frequency (an increase in reaction time) of each individual, others focused on the optimization or change of the perception and interaction system, but as individual‐based models are very suitable for parallel execution, many researchers adapted techniques from highly parallel processing . The latter is most commonly achieved via general‐purpose computing on graphics processing units (GPGPU).…”

Section: Introductionmentioning

confidence: 99%

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A hybrid model for simulating grazing herds in real time

Demšar

Blewitt

Bajec

2019

Computer Animation & Virtual

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Computer simulations of animal groups are usually performed via individual‐based modelling, where simulated animals are designed on the level of individuals. With this approach, developers are able to capture behavioural nuances of real animals. However, modelling each individual as its own entity has the downside of having a high computational cost, meaning that individual‐based models are usually not suitable for real‐time simulations of very large groups. A common alternative approach is flow‐based modelling, where the dynamics of animal congregations are designed on the group level. This enables researchers to create real‐time simulations of massive phenomena at the cost of biological authenticity. A novel approach called hybrid modelling tries to mix the best of both worlds—precision of individual‐based models and speed of flow‐based ones. An unknown surrounding hybrid model is the question of their biological authenticity and relevance. In this study, we develop a hybrid model for the simulation of herds of grazing sheep. Through Bayesian data analysis, we show that such an approach can encompass several aspects of real‐world sheep behaviour. Our hybrid model is also extremely efficient, capable of simulating herds of more than 1,000 individuals in real time without resorting to graphics processing unit execution.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A hybrid model for simulating grazing herds in real time

Demšar

Blewitt

Bajec

2019

Computer Animation & Virtual

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“…Related work has focused on high performance implementations of agent based simulation tools within distributed environments, 2,5 or alternatively has sought to accelerate simulations within particular domains or for specific models. [27][28][29] There have been other recent works which focus on software engineering challenges related to agent based simulation tools, in particular making software accessible in high level languages such as Python. 30,31 This article is unique in attempting to address the software engineering challenge of providing a general purpose simulator capable of utilising GPU acceleration.…”

Section: Discussionmentioning

confidence: 99%

“…FLAME GPU 2 is inspired by its predecessors FLAME GPU 1 and FLAME (for distributed CPUs) and shares the common goal of most generalised ABM simulators, of providing a layer of abstraction which permits modellers to focus on the development of agent based models rather than on their implementation. Related work has focused on high performance implementations of agent based simulation tools within distributed environments, 2,5 or alternatively has sought to accelerate simulations within particular domains or for specific models 27‐29 . There have been other recent works which focus on software engineering challenges related to agent based simulation tools, in particular making software accessible in high level languages such as Python 30,31 .…”

Section: Discussionmentioning

confidence: 99%

FLAME GPU 2: A framework for flexible and performant agent based simulation on GPUs

et al. 2023

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Agent based modelling (ABM) offers a powerful abstraction for scientific study in a broad range of domains. The use of agent based simulators encourages good software engineering design such as separation of concerns, that is, the uncoupling of the model description from its implementation detail. A major limitation in current approaches to ABM simulation is that of the trade off between simulator flexibility and performance. It is common that highly optimised simulations, such as those which target graphics processing units (GPU) hardware, are implemented as standalone software. This work presents a software framework (FLAME GPU 2) which balances flexibility with performance for general purpose ABM. Methods for ensuring high computational efficacy are demonstrated by, minimising data movement, and ensuring high device utilisation by exploiting opportunities for concurrent code execution within a model and through the use of ensembles of simulations. A novel hierarchical sub‐modelling approach is also presented which can be used to model certain types of recursive behaviours. This feature is shown to be essential in providing a mechanism to resolve competition for resources between agents within a parallel environment which would otherwise introduce race conditions. To understand the performance characteristics of the software, a benchmark model with millions of agents is used to explore the use of simulation ensembles and to parametrically investigate concurrent code execution within a model. Performance speedups are demonstrated of 3.5prefix×$$ \times $$ and 10prefix×$$ \times $$ respectively over a baseline GPU implementation. Our hierarchical sub‐modelling approach is used to demonstrate the implementation of a recursive algorithm to resolve competition of agent movement which occurs as a result of agent desire to simultaneously occupy discrete areas high in a ‘resource’. The algorithm is used to implement a classical socio‐economics model, Sugarscape, with populations of up to 16M agents.

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“…If care is not taken, this can end up as the bottleneck in the computation. For example, in the work described in [37], multiple flocks of Boids model could be simulated but the total numbers of agents cannot scale because of the use of a naïve approach (all-pairs) to find neighbors that has a complexity of OðN 2 Þ. MASON has a good implementation of continuous space that supports efficient finding neighbors by the discretization of the continuous space. Our implementation on GPUs supports this efficient method.…”

Section: Continuous Spacementioning

confidence: 99%