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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Vydané v:	Journal of computational physics Ročník 230; číslo 7; s. 2562 - 2574
Hlavní autori:	Jeschke, Matthias, Ewald, Roland, Uhrmacher, Adelinde M.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Kidlington Elsevier Inc 01.04.2011 Elsevier
Predmet:	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
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Abstract	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.
AbstractList	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 [tao]-leaping variant (S[tao]) that extends the basic leap method. S[tao] 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. 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 {tau}-leaping variant (S{tau}) that extends the basic leap method. S{tau} 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. 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.
Author	Jeschke, Matthias Ewald, Roland Uhrmacher, Adelinde M.
Author_xml	– sequence: 1 givenname: Matthias surname: Jeschke fullname: Jeschke, Matthias email: matthias.jeschke@uni-rostock.de, matthjes@googlemail.com – sequence: 2 givenname: Roland surname: Ewald fullname: Ewald, Roland email: roland.ewald@uni-rostock.de – sequence: 3 givenname: Adelinde M. surname: Uhrmacher fullname: Uhrmacher, Adelinde M. email: adelinde.uhrmacher@uni-rostock.de
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Snippet	Since the publication of Gillespie’s direct method, diverse methods have been developed to improve the performance of stochastic simulation methods and to... Since the publication of Gillespie's direct method, diverse methods have been developed to improve the performance of stochastic simulation methods and to...
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SubjectTerms	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
Title	Exploring the performance of spatial stochastic simulation algorithms
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