Oscar Key(He/Him)University College London
Oscar is a final-year PhD student at University College London working on computationally efficient machine learning. He is interested both in large-scale algorithms, which can be distributed across high-performance computing facilities, and approaches that make state-of-the-art models more accessible to practitioners and researchers with only limited resources. Before starting research, Oscar studied computer science and worked as a software engineer. |
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TalksScalable Data Assimilation with Message Passing23-Apr-24Data 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. |