Massively parallel programming in statistical optimization & simulation

General purpose graphics processing units (GPGPUs) suitable for general purpose programming have become sufficiently affordable in the last three years to be used in personal workstations. In this paper we assess the usefulness of such hardware in the statistical analysis of simulation input and output data. In particular we consider the fitting of complex parametric statistical metamodels to large data samples where optimization of a statistical function of the data is needed and investigate whether use of a GPGPU in such a problem would be worthwhile. We give an example, involving loss-given-default data obtained in a real credit risk study, where use of Nelder-Mead optimization can be efficiently implemented using parallel processing methods. Our results show that significant improvements in computational speed of well over an order of magnitude are possible. With increasing interest in "big data" samples the use of GPGPUs is therefore likely to become very important.