Gaussian Process Models with Parallelization and GPU acceleration
Distributed, Parallel, and Cluster Computing
2014-10-21 v1 Machine Learning
Machine Learning
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.
Cite
@article{arxiv.1410.4984,
title = {Gaussian Process Models with Parallelization and GPU acceleration},
author = {Zhenwen Dai and Andreas Damianou and James Hensman and Neil Lawrence},
journal= {arXiv preprint arXiv:1410.4984},
year = {2014}
}