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cs.DC2023
AMSP: Reducing Communication Overhead of ZeRO for Efficient LLM Training
Qiaoling Chen, Qinghao Hu, Guoteng Wang +8
Training large language models (LLMs) encounters challenges in GPU memory consumption due to the high memory requirements of model states. The widely used Zero Redundancy Optimizer…
cs.DC2023★ 4 cited
Boosting Distributed Full-graph GNN Training with Asynchronous One-bit Communication
Meng Zhang, Qinghao Hu, Peng Sun +2
Training Graph Neural Networks (GNNs) on large graphs is challenging due to the conflict between the high memory demand and limited GPU memory. Recently, distributed full-graph GNN…