1 citations · 3 across the 13 of their papers we have counts for
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Odin: Primitive-Level Synchronization for Distributed Point-Based Neural Rendering
Zhenxiang Ma, Zeyu He, Yuanzhen Zhou +6
Point-based neural rendering (PBNR) represents 3D scenes as explicit, trainable primitives and underpins high-quality reconstruction and emerging embodied AI and world-model pipeli…
A Flexible Programmable Pipeline Parallelism Framework for Efficient DNN Training
Lijuan Jiang, Xingjian Qian, Zhenxiang Ma +4
Pipeline parallelism is an essential distributed parallelism method. Increasingly complex and diverse DNN models necessitate meticulously customized pipeline schedules for performa…
ZeroPP: Unleashing Exceptional Parallelism Efficiency through Tensor-Parallelism-Free Methodology
Ding Tang, Lijuan Jiang, Jiecheng Zhou +5
Large-scale models rely heavily on 3D parallelism for distributed training, which utilizes tensor parallelism (TP) as the intra-operator parallelism to partition model states acros…