  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.
