Gaussian Process Models with Parallelization and GPU acceleration

Abstract

In this work, we present an extension of Gaussian process (GP) models with sophisticated parallelization and GPU acceleration. The parallelization scheme arises naturally from the modular computational structure w.r.t. datapoints in the sparse Gaussian process formulation. Additionally, the computational bottleneck is implemented with GPU acceleration for further speed up. Combining both techniques allows applying Gaussian process models to millions of datapoints. The efficiency of our algorithm is demonstrated with a synthetic dataset. Its source code has been integrated into our popular software library GPy.

References in corpus (2)

• Deep Gaussian Processes
• Variational Inference for Uncertainty on the Inputs of Gaussian Process Models

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