Parallel computation of Gaussian processes

Within the Bayesian framework we utilize Gaussian processes for parametric studies of long running computer codes. Since the simulations are expensive it is necessary to exploit the computational budget in the best possible manner. Employing the sum over variances – being indicators for the quality of the fit – as the utility function we established an optimized and automated sequential parameter selection procedure. However, often it is also desirable to utilize the parallel running capabilities of present computer technology and abandon the sequential parameter selection for a faster overall turn-around time (wall-clock time). The paper proposes to achieve this by marginalizing over the expected outcomes at optimized test points in order to set up a pool of starting values for batch execution.