Data assimilation is a core component of numerical weather prediction systems. The large quantity of data processed during assimilation requires the computation to be distributed across increasingly many compute nodes, yet existing approaches suffer from synchronisation overhead in this setting. In this paper, we exploit the formulation of data assimilation as a Bayesian inference problem and apply a message-passing algorithm to solve the spatial inference problem. Since message passing is inherently based on local computations, this approach lends itself to parallel and distributed computation. In combination with a GPU-accelerated implementation, we can scale the algorithm to very large grid sizes while retaining good accuracy and compute and memory requirements.

翻译：数据同化是数值天气预报系统的核心组成部分。同化过程中处理的大量数据要求计算分布在越来越多的计算节点上，然而现有方法在这种场景下存在同步开销问题。本文利用数据同化在贝叶斯推断框架下的数学表达，通过应用消息传递算法解决空间推断问题。由于消息传递本质上基于局部计算，该方法天然适用于并行与分布式计算。结合GPU加速实现，我们能够在保持良好精度以及计算与内存需求的前提下，将算法扩展到极大网格规模。