The content discusses the challenges of Gaussian processes in geospatial analysis due to high computational complexity. It introduces the Vecchia approximation method and its parallel implementation on GPUs, showcasing significant speed improvements and accuracy preservation. The study evaluates the algorithm's performance with real datasets, highlighting optimal settings for accuracy while reducing memory complexity.
The content emphasizes the importance of spatial ordering in log-likelihood approximation and explores the impact of range and smoothness parameters on approximation difficulty. It also delves into numerical studies comparing exact MLE with Vecchia approximations, showcasing reduced computational complexity and memory requirements. Additionally, real data assessments on soil moisture and wind speed datasets demonstrate accurate predictions with the Vecchia algorithm.
Performance assessments across different GPU architectures reveal efficient execution using batched operations, achieving significant speedups compared to traditional methods. Overall, the content provides insights into optimizing geospatial data analysis through GPU-accelerated Vecchia approximations.
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by Qilong Pan,S... pada arxiv.org 03-13-2024
https://arxiv.org/pdf/2403.07412.pdfPertanyaan yang Lebih Dalam