activity
20192025
most citedSparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks

341 citations · 608 across the 18 of their papers we have counts for

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Showing 2024Show all

2 papers · 1 filter

cs.LG2024

Lion Cub: Minimizing Communication Overhead in Distributed Lion

Satoki Ishikawa, Tal Ben-Nun, Brian Van Essen +2

Communication overhead is a key challenge in distributed deep learning, especially on slower Ethernet interconnects, and given current hardware trends, communication is likely to b…

cs.DC2024★ 2 cited

Scaling Large-scale GNN Training to Thousands of Processors on CPU-based Supercomputers

Chen Zhuang, Lingqi Zhang, Du Wu +8

Graph Convolutional Networks (GCNs), particularly for large-scale graphs, are crucial across numerous domains. However, training distributed full-batch GCNs on large-scale graphs s…