most citedA Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges

6 citations · 6 across the 4 of their papers we have counts for

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cs.DC2024

Frenzy: A Memory-Aware Serverless LLM Training System for Heterogeneous GPU Clusters

Zihan Chang, Sheng Xiao, Shuibing He +3

Existing work only effective on a given number of GPUs, often neglecting the complexities involved in manually determining the specific types and quantities of GPUs needed, which c…

cs.DC2024

HopGNN: Boosting Distributed GNN Training Efficiency via Feature-Centric Model Migration

Weijian Chen, Shuibing He, Haoyang Qu +1

Distributed training of graph neural networks (GNNs) has become a crucial technique for processing large graphs. Prevalent GNN frameworks are model-centric, necessitating the trans…

cs.DC2024★ 1 cited

Optimizing Large Model Training through Overlapped Activation Recomputation

Ping Chen, Wenjie Zhang, Shuibing He +10

Large model training often uses recomputation to alleviate memory pressure and pipelines to exploit the parallelism of data, tensors, and devices. However, existing recomputation a…