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Found 3 papers, 1 papers with code
Scalable Training of Trustworthy and Energy-Efficient Predictive Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN
The HydraGNN architecture enables the GFM to achieve near-linear strong scaling performance using more than 2, 000 GPUs on Perlmutter and 16, 000 GPUs on Frontier.
Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules
Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures.
ADIOS 2: The Adaptable Input Output System. A framework for high-performance data management
We present ADIOS 2, the latest version of the Adaptable Input Output (I/O) System.
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