Scalable Data Assimilation with Message Passing
CoRR(2024)
Abstract
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.
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