Modeling for Large Datasets using Graphics Processing Units

Abstract: The graphics processing unit (GPU) has emerged as a power-ful 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 expen-sive. 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 sys-tem with no GPU acceleration. Our small study suggests that GP models are fertile ground for further implementation on CPU+GPU systems