Time filter

Source Type

Hill D.R.C.,Clermont University | Hill D.R.C.,CNRS Laboratory of Informatics, Modeling and Optimization of Systems | Hill D.R.C.,ISIMA Computer Science and Modeling Institute | Mazel C.,Clermont University | And 8 more authors.
Concurrency Computation Practice and Experience | Year: 2013

There is an increasing interest in the distribution of parallel random number streams in the high-performance computing community particularly, with the manycore shift. Even if we have at our disposal statistically sound random number generators according to the latest and thorough testing libraries, their parallelization can still be a delicate problem. Indeed, a set of recent publications shows it still has to be mastered by the scientific community. With the arrival of multi-core and manycore processor architectures on the scientist desktop, modelers who are non-specialists in parallelizing stochastic simulations need help and advice in distributing rigorously their experimental plans and replications according to the state of the art in pseudo-random numbers parallelization techniques. In this paper, we discuss the different partitioning techniques currently in use to provide independent streams with their corresponding software. In addition to the classical approaches in use to parallelize stochastic simulations on regular processors, this paper also presents recent advances in pseudo-random number generation for general-purpose graphical processing units. The state of the art given in this paper is written for simulation practitioners. Copyright © 2012 John Wiley & Sons, Ltd.

Loading ISIMA Computer Science and Modeling Institute collaborators
Loading ISIMA Computer Science and Modeling Institute collaborators