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Stochastic Analysis of a Queue Length Model Using a Graphics Processing Unit

J. Prikryl, J. Kocijan
Faculty of Transportation Sciences, Czech University of Technology, Prague, Czech Republic
Transactions on Transport Sciences, Volume 5, Number 2, 2012

@article{pvrikryl2012stochastic,

   title={Stochastic Analysis of a Queue Length Model Using a Graphics Processing Unit},

   author={P{v{r}}ikryl, J. and Kocijan, J.},

   journal={Transactions on Transport Sciences},

   volume={5},

   number={2},

   pages={55–62},

   year={2012},

   publisher={Versita}

}

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Mathematical modeling is an inevitable part of system analysis and design in science and engineering. When a parametric mathematical description is used, the issue of the parameter estimation accuracy arises. Models with uncertain parameter values can be evaluated using various methods and computer simulation is among the most popular in the engineering community. Nevertheless, an exhaustive numerical analysis of models with numerous uncertain parameters requires a substantial computational effort. The purpose of this paper is to show how the computation can be accelerated using a parallel configuration of graphics processing units (GPU). The assessment of the computational speedup is illustrated with a case study. The case study is a simulation of Highway Capacity Manual 2000 Queue Model with selected uncertain parameters. The computational results show that the parallel computation solution is efficient for a larger amount of samples when the initial and communication overhead of parallel computation becomes a sufficiently small part of the whole process.
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