# Gaussian Process Models with Parallelization and GPU acceleration

arXiv: https://arxiv.org/abs/1410.4984 · HTML: https://mlsanity.com/paper/1410.4984/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2014.

## Built on by (most cited first, 3 of 3)

- [Diverse Video Generation using a Gaussian Process Trigger](https://mlsanity.com/paper/2107.04619/) (2021)
- [Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation](https://mlsanity.com/paper/2205.10879/) (2022)
- [Implementation and Analysis of GPU Algorithms for Vecchia Approximation](https://mlsanity.com/paper/2407.02740/) (2024)

## Abstract (arXiv)

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.