ShardTensor is a domain-parallelism system for SciML that enables flexible scaling of extreme-resolution spatial datasets by removing the constraint of batch size one per device.
Global cloud-resolving models
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ICON and IFS-FESOM capture the global distribution of Köppen-Geiger climate categories but show regional biases dominated by precipitation inaccuracies, and inter-model differences exceed the 30-year climate change signal in many zones.
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ShardTensor: Domain Parallelism for Scientific Machine Learning
ShardTensor is a domain-parallelism system for SciML that enables flexible scaling of extreme-resolution spatial datasets by removing the constraint of batch size one per device.
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Evaluating local climate in global storm-resolving models with the K\"oppen-Geiger classification
ICON and IFS-FESOM capture the global distribution of Köppen-Geiger climate categories but show regional biases dominated by precipitation inaccuracies, and inter-model differences exceed the 30-year climate change signal in many zones.