  The graphics processing unit (GPU) has emerged as a powerful and cost
effective processor for general performance computing. GPUs are capable of an
order of magnitude more floating-point operations per second as compared to
modern central processing units (CPUs), and thus provide a great deal of
promise for computationally intensive statistical applications. Fitting complex
statistical models with a large number of parameters and/or for large datasets
is often very computationally expensive. In this study, we focus on Gaussian
process (GP) models -- statistical models commonly used for emulating expensive
computer simulators. We demonstrate that the computational cost of implementing
GP models can be significantly reduced by using a CPU+GPU heterogeneous
computing system over an analogous implementation on a traditional computing
system with no GPU acceleration. Our small study suggests that GP models are
fertile ground for further implementation on CPU+GPU systems.
