Parallel implementations of individual-based models in biology: bulletin- and non-bulletin-board approaches

Nugala, Vasudevarao; Allan, Stephen J.; Haefner, James W.

doi:10.1016/s0303-2647(97)00069-5

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…Current simulations using a Pentium ® 4 CPU 3.4 GHz duo-processor took over an hour to simulate one day when numbers of animals reached 100,000. Parallel programming offers some opportunities (Nugala et al, 1998). Parry and Evans (2008) concluded that for the simulation of very large populations parallel programming is a good solution.…”

Section: Model Limitations and Recommendations For Improvementmentioning

confidence: 99%

Eco-SpaCE: An object-oriented, spatially explicit model to assess the risk of multiple environmental stressors on terrestrial vertebrate populations

Loos

Ragas

Plasmeijer

et al. 2010

Science of The Total Environment

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Section: Model Limitations and Recommendations For Improvementmentioning

confidence: 99%

Eco-SpaCE: An object-oriented, spatially explicit model to assess the risk of multiple environmental stressors on terrestrial vertebrate populations

Loos

Ragas

Plasmeijer

et al. 2010

Science of The Total Environment

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“…In Janson et al (2005), the work by Nugala et al (1998) is noted as one of the first parallel implementations of ant‐based clustering. In the study, they compare two parallelization approaches for an ant‐based clustering technique where the ants move on a two‐dimensional grid.…”

Section: Related Workmentioning

confidence: 99%

Parallelization of an ant‐based clustering approach

Gunes

Uyar

2010

Kybernetes

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PurposeThe purpose of this paper is to propose parallelization of a successful sequential ant‐based clustering algorithm (SABCA) to increase time performance.Design/methodology/approachA SABCA is parallelized through the chosen parallelization library MPI. Parallelization is performed in two stages. In the first stage, data to be clustered are divided among processors. After the sequential ant‐based approach running on each processor clusters the data assigned to it, the resulting clusters are merged in the second stage. The merging is also performed through the same ant‐based technique. The experimental analysis focuses on whether the implemented parallel ant‐based clustering method leads to a better time performance than its fully sequential version or not. Since the aim of this paper is to speedup the time consuming, but otherwise successful, ant‐based clustering method, no extra steps are taken to improve the clustering solution. Tests are executed using 2 and 4 processors on selected sample datasets. Results are analyzed through commonly used cluster validity indices and parallelization performance metrices.FindingsAs a result of the experiments, it is seen that the proposed algorithm performs better based on time measurements and parallelization performance metrices; as expected, it does not improve the clustering quality based on the cluster validity indices. Furthermore, the communication cost is very small compared to other ant‐based clustering parallelization techniques proposed so far.Research limitations/implicationsThe use of MPI for the parallelization step has been very effective. Also, the proposed parallelization technique is quite successful in increasing time performance; however, as a future study, improvements to clustering quality can be made in the final step where the partially clustered data are merged.Practical implicationsThe results in literature show that ant‐based clustering techniques are successful; however, their high‐time complexity prohibit their effective use in practical applications. Through this low‐communication‐cost parallelization technique, this limitation may be overcome.Originality/valueA new parallelization approach to ant‐based clustering is proposed. The proposed approach does not decrease clustering performance while it increases time performance. Also, another major contribution of this paper is the fact that the communication costs required for parallelization is lower than the previously proposed parallel ant‐based techniques.

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“…For example, parallel simulation techniques (particularly ABM/IBM) have been applied to the study of animal and human movement (see Uziel and Berry 1996, Abbott et al. 1997, Nugala et al. 1998, Nagel and Rickert 2001), vegetation dynamics (Cornwell et al.…”

Section: Introductionmentioning

confidence: 99%

HPABM: A Hierarchical Parallel Simulation Framework for Spatially‐explicit Agent‐based Models

Tang

Wang

2009

Transactions in GIS

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A Hierarchical Parallel simulation framework for spatially‐explicit Agent‐Based Models (HPABM) is developed to enable computationally intensive agent‐based models for the investigation of large‐scale geospatial problems. HPABM allows for the utilization of high‐performance and parallel computing resources to address computational challenges in agent‐based models. Within HPABM, an agent‐based model is decomposed into a set of sub‐models that function as computational units for parallel computing. Each sub‐model is comprised of a sub‐set of agents and their spatially‐explicit environments. Sub‐models are aggregated into a group of super‐models that represent computing tasks. HPABM based on the design of super‐ and sub‐models leads to the loose coupling of agent‐based models and underlying parallel computing architectures. The utility of HPABM in enabling the development of parallel agent‐based models was examined in a case study. Results of computational experiments indicate that HPABM is scalable for developing large‐scale agent‐based models and, thus, demonstrates efficient support for enhancing the capability of agent‐based modeling for large‐scale geospatial simulation.

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Parallel implementations of individual-based models in biology: bulletin- and non-bulletin-board approaches

Cited by 7 publications

References 20 publications

Eco-SpaCE: An object-oriented, spatially explicit model to assess the risk of multiple environmental stressors on terrestrial vertebrate populations

Eco-SpaCE: An object-oriented, spatially explicit model to assess the risk of multiple environmental stressors on terrestrial vertebrate populations

Parallelization of an ant‐based clustering approach

HPABM: A Hierarchical Parallel Simulation Framework for Spatially‐explicit Agent‐based Models

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