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Yu-Chong Hu

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Open access Jul 2026

Enabling Efficient All-Dimension Top-k Sparsification for High-Performance Distributed DNN Training Systems

ADTopk is proposed, an all-dimension top-k sparsification scheme that selects the k largest elements across all dimensions of the per-layer gradient tensor, ensuring that every dimension contributes at least one element and eliminating dimension missing.

Zhangqiang Ming, Yuchong Hu, Patrick P. C. Lee et al. · 0 citations
Open access Jul 2026

Enabling Efficient All-Dimension Top-k Sparsification for High-Performance Distributed DNN Training Systems

Distributed deep neural network (DNN) training across multiple workers is widely adopted to accelerate the training of large models and datasets. However, their performance is often limited by the synchronization of inter-worker data (i.e., gradient tensors). Top-k sparsification serves as the de facto compression appr...

Zhangqiang Ming, Yuchong Hu, Patrick P. C. Lee et al. · 0 citations

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