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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.
L3: Accelerator-Friendly Lossless Image Format for High-Resolution, High-Throughput DNN Training
The decoding process of L3 is effectively parallelized on the accelerator, thus minimizing CPU intervention for data preparation during DNN training.
Faster and Smarter AutoAugment: Augmentation Policy Search Based on Dynamic Data-Clustering
Data augmentation tuned to datasets and tasks has had great success in various AI applications, such as computer vision, natural language processing, autonomous driving, and bioinformatics.
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