Exploring the performance of spatial stochastic simulation algorithms

Since the publication of Gillespie’s direct method, diverse methods have been developed to improve the performance of stochastic simulation methods and to enter the spatial realm. In this paper we discuss a spatial τ-leaping variant (S τ) that extends the basic leap method. S τ takes reaction and bo...

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Veröffentlicht in:Journal of computational physics Jg. 230; H. 7; S. 2562 - 2574
Hauptverfasser: Jeschke, Matthias, Ewald, Roland, Uhrmacher, Adelinde M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Inc 01.04.2011
Elsevier
Schlagworte:
ALGORITHMS
Benchmarking
BENCHMARKS
COMPARATIVE EVALUATIONS
Computation
Computational techniques
Computer simulation
COMPUTERIZED SIMULATION
Design engineering
Diffusion
EVALUATION
Exact sciences and technology
Gillespie multi-particle method
MATHEMATICAL LOGIC
MATHEMATICAL METHODS AND COMPUTING
Mathematical methods in physics
Mathematical models
Next sub-volume method
Performance analysis
Performance evaluation
Physics
SIMULATION
Spatial τ-leaping
SSA
STOCHASTIC PROCESSES
Stochastic simulation
Stochasticity
τ-Leaping
SSA
Performance evaluation
τ-Leaping
Next sub-volume method
Stochastic simulation
Gillespie multi-particle method
Performance analysis
Spatial τ-leaping
Spatial analysis
Stochastic method
Calculation methods
Algorithms
Models
Calculation
Diffusion
Performance
ISSN:0021-9991, 1090-2716
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Since the publication of Gillespie’s direct method, diverse methods have been developed to improve the performance of stochastic simulation methods and to enter the spatial realm. In this paper we discuss a spatial τ-leaping variant (S τ) that extends the basic leap method. S τ takes reaction and both outgoing and incoming diffusion events into account when calculating a leap candidate. A performance analysis shall reveal details on the achieved success in balancing speed and accuracy in comparison to other methods. However, performance analysis of spatial stochastic algorithms requires significant effort — it is crucial to choose suitable (benchmark) models and to carefully define model and simulation setups that take problem and simulation design spaces into account.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:0021-9991
1090-2716
DOI:10.1016/j.jcp.2010.12.030