Gaussian Process Models with Parallelization and GPU acceleration
This work addresses the computational bottleneck in Gaussian process modeling for large datasets, though it is incremental as it builds on existing sparse formulations.
The authors tackled the scalability of Gaussian process models by introducing parallelization and GPU acceleration, enabling application to millions of datapoints, as demonstrated on a synthetic dataset.
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.