  Stochastic simulations are often sensitive to the source of randomness that
character-izes the statistical quality of their results. Consequently, we need
highly reliable Random Number Generators (RNGs) to feed such applications.
Recent developments try to shrink the computa-tion time by relying more and
more General Purpose Graphics Processing Units (GP-GPUs) to speed-up stochastic
simulations. Such devices bring new parallelization possibilities, but they
also introduce new programming difficulties. Since RNGs are at the base of any
stochastic simulation, they also need to be ported to GP-GPU. There is still a
lack of well-designed implementations of quality-proven RNGs on GP-GPU
platforms. In this paper, we introduce ShoveRand, a frame-work defining common
rules to generate random numbers uniformly on GP-GPU. Our framework is designed
to cope with any GPU-enabled development platform and to expose a
straightfor-ward interface to users. We also provide an existing RNG
implementation with this framework to demonstrate its efficiency in both
development and ease of use.
