Year: 2014
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Dai, Zhenwen, Damianou, Andreas, Hensman, James, Lawrence, Neil
Institutions: University of Sheffield, Lancaster University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: G8538723892
OpenAlex ID: W1876599412