Parallel Gaussian process with kernel approximation in CUDA
<|reference_start|>Parallel Gaussian process with kernel approximation in CUDA: This paper introduces a parallel implementation in CUDA/C++ of the Gaussian process with a decomposed kernel. This recent formulation, introduced by Joukov and Kuli\'c (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.<|reference_end|>
@article{carminati2024parallel,
    title={Parallel Gaussian process with kernel approximation in CUDA},
    author={Davide Carminati},
    journal={arXiv preprint arXiv:2403.12797},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12797},
    primaryClass={cs.DC}
}