Many scientific phenomena are studied using computer experiments consisting of multiple runs a model while varying the input settings. Gaussian processes (GPs) popular tool for analysis experiments, enabling interpolation between settings, but direct GP inference is computationally infeasible large datasets. We adapt and extend powerful class methods from spatial statistics to enable scalable e...