3 papers
cs.DC2026
Cross-Layer Energy Analysis of Multimodal Training on Grace Hopper Superchips
Mahmoud Ahmed, Sameh Abdulah, Olatunji Ruwase +4
Multimodal deep learning models enable joint learning across heterogeneous data sources, including text, images, and video, but their rapid scaling introduces significant memory an…
cs.LG2025
FlashDP: Private Training Large Language Models with Efficient DP-SGD
Liangyu Wang, Junxiao Wang, Jie Ren +3
As large language models (LLMs) increasingly underpin technological advancements, the privacy of their training data emerges as a critical concern. Differential Privacy (DP) serves…
cs.LG2025
ZO2: Scalable Zeroth-Order Fine-Tuning for Extremely Large Language Models with Limited GPU Memory
Liangyu Wang, Jie Ren, Hang Xu +4
Fine-tuning large pre-trained LLMs generally demands extensive GPU memory. Traditional first-order optimizers like SGD encounter substantial difficulties due to increased memory re…