Accelerating the Stochastic Simulation Algorithm

David Jenkins, Gregory D. Peterson
Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee, USA
Symposium on Application Accelerators in High Performance Computing, 2009 (SAAHPC’09)


   title={Accelerating the Stochastic Simulation Algorithm},

   author={Jenkins, D. and Peterson, G.D.},

   booktitle={Symposium on Application Accelerators in High-Performance Computing, Urbana, IL},



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In order for scientists to learn more about molecular biology, it is imperative that they have the ability to construct accurate models that predict the reactions of species of molecules. Generating these models using deterministic approaches is not feasible as these models may violate some of the assumptions underlying classical differential equations models (e.g., small populations with discrete values). Statistics consistent with the chemical master equation can be obtained using Gillespie’s stochastic simulation algorithm (SSA). Due to the stochastic nature of the Monte Carlo simulations, large numbers of simulations must be run in order to get accurate statistics on the species and reactions. However, the algorithm tends to be computationally heavy and leads to long simulation runtimes for large systems. In this paper, we provide an approach to running these simulations using MPI and NVIDIA graphics processing units using CUDA in order to parallelize these simulations, reducing the total amount of time needed for multiple simulations to run in a more reasonable time scale.
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