Fat versus Thin Threading Approach on GPUs: Application to Stochastic Simulation of Chemical Reactions

Guido Klingbeil, Radek Erban, Mike Giles, Philip K. Maini

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


We explore two different threading approaches on a graphics processing unit (GPU) exploiting two different characteristics of the current GPU architecture. The fat thread approach tries to minimize data access time by relying on shared memory and registers potentially sacrificing parallelism. The thin thread approach maximizes parallelism and tries to hide access latencies. We apply these two approaches to the parallel stochastic simulation of chemical reaction systems using the stochastic simulation algorithm (SSA) by Gillespie [14]. In these cases, the proposed thin thread approach shows comparable performance while eliminating the limitation of the reaction system's size. © 2006 IEEE.
Original languageEnglish (US)
Pages (from-to)280-287
Number of pages8
JournalIEEE Transactions on Parallel and Distributed Systems
Issue number2
StatePublished - Feb 2012
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUK-C1-013-04
Acknowledgements: GK was supported by the Systems biology Doctoral Training Center (DTC) and the Engineering and Physical Sciences Research Council (EPSRC). 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. RE would also like to thank Somerville College, University of Oxford for Fulford Junior Research Fellowship. MG was supported in part by the Oxford-Man Institute of Quantitative Finance, and by the United Kingdom Engineering and Physical Sciences Research Council under research grant EP/G00210X/. PM was partially supported by a Royal Wolfson Merit Award.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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