GraphGP: Scalable Gaussian Processes with Vecchia's Approximation
Researchers have developed GraphGP, a GPU-accelerated algorithm designed to make Gaussian processes more scalable. This new method utilizes Vecchia's approximation to reduce the computational complexity from cubic to linear, enabling the handling of nearly a billion parameters. Key innovations include a novel bit-reversed k-d tree ordering for efficient neighbor searches and parallel processing, alongside a differentiable CUDA implementation that significantly outperforms existing JAX baselines in speed and memory usage. AI
IMPACT Enables larger-scale applications of Gaussian processes in machine learning and scientific modeling.