STOCHSIMGPU: parallel stochastic simulation for the Systems Biology Toolbox 2 for MATLAB

G. Klingbeil, R. Erban, M. Giles, P. K. Maini

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Motivation: The importance of stochasticity in biological systems is becoming increasingly recognized and the computational cost of biologically realistic stochastic simulations urgently requires development of efficient software. We present a new software tool STOCHSIMGPU that exploits graphics processing units (GPUs) for parallel stochastic simulations of biological/chemical reaction systems and show that significant gains in efficiency can be made. It is integrated into MATLAB and works with the Systems Biology Toolbox 2 (SBTOOLBOX2) for MATLAB. Results: The GPU-based parallel implementation of the Gillespie stochastic simulation algorithm (SSA), the logarithmic direct method (LDM) and the next reaction method (NRM) is approximately 85 times faster than the sequential implementation of the NRM on a central processing unit (CPU). Using our software does not require any changes to the user's models, since it acts as a direct replacement of the stochastic simulation software of the SBTOOLBOX2. © The Author 2011. Published by Oxford University Press. All rights reserved.
Original languageEnglish (US)
Pages (from-to)1170-1171
Number of pages2
Issue number8
StatePublished - Feb 25 2011
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUK-C1-013-04
Acknowledgements: G.K. was supported by the Systems Biology Doctoral Training Centre and the Engineering and Physical Sciences Research Council. This publication was based on work supported in part by Award No KUK-C1-013-04, made by King Abdullah University of Science and Technology (KAUST). The research leading to these results has received funding from the European Research Council under the European Community's Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement No. 239870. R.E. would also like to thank Somerville College, University of Oxford, for a Fulford Junior Research Fellowship. M.G. was supported in part by the Oxford-Man Institute of Quantitative Finance, and by the UK Engineering and Physical Sciences Research Council under research grant (EP/G00210X/). P.K.M. was partially supported by a Royal Society Wolfson Research Merit Award.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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