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Adaptive Workload-Balanced Scheduling Strategy for Global Ocean Data Assimilation on Massive GPUs
SessionAlgorithms on GPUs
DescriptionGlobal ocean data assimilation is a crucial technique to estimate the actual oceanic state by combining numerical model outcomes and observation data, which is widely used in climate research. Due to the imbalanced distribution of observation data in global ocean, the parallel efficiency of recent methods suffers from workload imbalance. When massive GPUs are applied for global ocean data assimilation, the workload imbalance becomes more severe, resulting in poor scalability. In this work, we propose a novel adaptive workload-balance scheduling strategy, assimilation, which successfully estimates the total workload prior to execution and ensures a balanced workload assignment. Further, we design a parallel dynamic programming approach to accelerate the schedule decision, and develop a factored dataflow to exploit the parallel potential of GPUs. Evaluation demonstrates that our algorithm outperforms the state-of-the-art method by up to 9.1x speedup. This work is the first to scale global ocean data assimilation to 4,000 GPUs.
Event Type
Paper
TimeThursday, 16 November 20232:30pm - 3pm MST
Location301-302-303
Accelerators
Algorithms
Graph Algorithms and Frameworks
TP
