The more parameters a model has the more runs are necessary to describe the response surface adequately. In this particular figure, parameters are taken as example and synonym for sources of uncertainty. For example, a two parameter model would need four simulations to sample a two by two grid. 100 simulations would be already necessary to sample a 10 by 10 grid. The amount of subdivision needed for each parameter would depend on the non-linearity of the response surface. The sampling increases with the number of parameters. For our example it could be computed by

NS: Number of samples D: Number of subdivisions in the parameter space Np: Number of parameters.

In this particular set-up the total processor run-time required would be the number of samples multiplied by the execution time of one model realisation. It has to be noted, that this is just a crude example as most uncertainty techniques have more efficient techniques to quantify the response surface.

In summary, this question depends highly on the type of model or model cascade used and should be based on previous experience. As a bold statement, we argue that a model with more than 8 parameters and an execution time of more than 2 minutes should be considered as computer intensive (if executed on a single CPU).