A Short Note on Gaussian Process Modeling for Large Datasets using Graphics Processing Units

Details A Short Note on Gaussian Process Modeling for Large Datasets using Graphics Processing Units A Short Note on Gaussian Process Modeling for Large Datasets using Graphics Processing Units

2012-03-06

arxiv.org/abs/1203.1269v1

Statistics

Computation

Scientific paper

The graphics processing unit (GPU) has emerged as a powerful and cost effective processor for high 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 computationally very 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 significantly be reduced by using a CPU+GPU heterogeneous computing system over an analogous implementation on a traditional computing system without GPU acceleration (i.e., CPU only). Our small study suggests that GP models are fertile ground for further implementation on CPU+GPU systems.

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