Gaussian Process Models with Parallelization and GPU acceleration

TL;DR

This paper introduces an enhanced Gaussian process modeling approach that leverages parallelization and GPU acceleration, enabling the application of GPs to datasets with millions of points, demonstrated through synthetic data experiments.

Contribution

The paper presents a novel combination of parallelization and GPU acceleration for Gaussian processes, significantly improving scalability and computational efficiency.

Findings

Successfully applied to datasets with millions of points

Achieved substantial speed-up over traditional methods

Integrated into the GPy library for wider use

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.