<ul data-eligibleForWebStory="true"><li>Research introduces the Distributed Cross-Channel Hierarchical Aggregation (D-CHAG) approach for vision-based scientific foundation models.</li><li>D-CHAG is designed to handle datasets with a large number of channels across image modalities and improve computational efficiency.</li><li>The approach was tested on hyperspectral imaging and weather forecasting tasks, showing significant memory reduction and increased throughput.</li><li>The study integrated D-CHAG with tensor parallelism and model sharding, achieving promising results on the Frontier Supercomputer.</li></ul>

Distributed Cross-Channel Hierarchical Aggregation for Foundation Models

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