Pseudo-random number generators for massively parallel discrete-event simulation. (2012)
A significant problem faced by scientific investigation of complex modern systems is that credible simulation studies of such systems on single computers can frequently not be finished in a feasible time. Discrete-event simulation of dynamic stochastic systems, allowing multiple replications in parallel (MRIP) to speed up simulation time, has become one of the most popular paradigms of investigation in many areas of science and engineering. One of the general problems related with distributed simulation is the need of parallel generation of multiple sequences of pseudo-random numbers across cooperating processors, with the number of known, good parallel generators being very limited. This report assesses currently known techniques proposed for generation of pseudo-random numbers in processing systems, particularly the statistical proper- ties of multiple sequences of numbers generated in parallel, and the speed of generation of these parallel streams and also the pseudo-random numbers themselves. Parallel implementations of the MRG32k3a and DX-120-2 generators are found to be the most suitable of those tested.
ANZSRC Fields of Research49 - Mathematical sciences::4905 - Statistics::490510 - Stochastic analysis and modelling
08 - Information and Computing Sciences::0803 - Computer Software
RightsCopyright Adam Freeth
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Freeth, A; Pawlikowski, K; McNickle, D (University of Canterbury, 2012)A signi cant problem faced by scienti c investigation of complex modern systems is that credible simulation studies of such systems on single computers can frequently not be nished in a feasible time. Discrete-event ...
Heuristic Rules for Improving Quality of Results from Sequential Stochastic Discrete-Event Simulation Pawlikowski, K.; McNickle, D.; Lee, J.-S. R. (University of Canterbury. Computer Science and Software Engineering, 2012)Sequential analysis of output data during stochastic discrete-event simulation is a very effective practical way of controlling statistical errors of final simulation results. Such stochastic sequential simulation evolves ...
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