0381577 20240103201322.620121030235959.9 10.2478/v10158-012-0007-2 8 s. cav_un_epca*03575921802-971X52 (2012)55-62 graphics processing unit GPU Monte Carlo simulation computer simulation modeling cav_un_auth*0205734 Přikryl Jan Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B G Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0255838 Kocijan J. SI http://library.utia.cas.cz/separaty/2012/AS/prikryl-stochastic analysis of a queue length model using a graphics processing unit.pdf MEB091015 GA MŠk CZ cav_un_auth*0263551 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 larger amount of samples when the initial and communication overhead of parallel computation becomes a sufficiently small part of the whole process. 2013 BC 2 RVO:67985556 http://hdl.handle.net/11104/0212013 2012 cav_un_epca*0357592 Transactions on Transport Sciences 1802-971X Roč. 5 č. 2 2012 55 62