Parallel Gaussian process with kernel approximation in CUDA
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso, Duca degli Abruzzi 24, Torino, 10129, Italy
arXiv:2403.12797 [cs.DC], (19 Mar 2024)
@misc{carminati2024parallel,
title={Parallel Gaussian process with kernel approximation in CUDA},
author={Davide Carminati},
year={2024},
eprint={2403.12797},
archivePrefix={arXiv},
primaryClass={cs.DC}
}
This paper introduces a parallel implementation in CUDA/C++ of the Gaussian process with a decomposed kernel. This recent formulation, introduced by Joukov and Kulić (2022), is characterized by an approximated — but much smaller — matrix to be inverted compared to plain Gaussian process. However, it exhibits a limitation when dealing with higher-dimensional samples which degrades execution times. The solution presented in this paper relies on parallelizing the computation of the predictive posterior statistics on a GPU using CUDA and its libraries. The CPU code and GPU code are then benchmarked on different CPU-GPU configurations to show the benefits of the parallel implementation on GPU over the CPU.
March 24, 2024 by hgpu
