cs.DC · 2026-07-23 · No. 62
Distributed, Parallel, and Cluster Computing, 2026-07-23.
4 new papers in cs.DC. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
4 entries-
01
Fully Dynamic Rooted Spanning Tree on GPU
Abhijeet Sahu, Harmit Singh, Soham Nandy, G. Ramakrishna
cs.DC · cs.DS
Spanning trees are fundamental structures in graph theory, essential for various applications such as network maintenance, routing adjustments, and many more. The dynamic nature of real-world networks requires efficient updates to these structures as the underlying graph evolves. Maintaining rooted spanning trees dynamically is particularly crucial for algorithms addressing 2-connected components and minimum-weighted spanning trees. In this...
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02
Ascend to Science: Exploration of AI Chips for Scientific Computing
Weicheng Xue, Kai Yang, Yongxiang Liu, Baisong Xu, Dengdong Fan, Xianglin Liu, Pengxiang Xu, Yonghong Tian
cs.DC
The rapid rise of AI-oriented accelerators has reshaped compute systems around low-precision tensor engines, raising a practical question for the HPC community: under what conditions can such hardware support scientific workloads that demand numerical robustness, irregular memory access, and scalability? Using the Ascend 910 NPU series as a representative tensor-centric platform, we characterize precision, execution, and memory-hierarchy...
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03
Odin: Primitive-Level Synchronization for Distributed Point-Based Neural Rendering
Zhenxiang Ma, Zeyu He, Yuanzhen Zhou, Zhenyu Yang, Yuchang Zhang, Miao Tao, Rong Fu, Jidong Zhai, Hengjie Li
cs.DC
Point-based neural rendering (PBNR) represents 3D scenes as explicit, trainable primitives and underpins high-quality reconstruction and emerging embodied AI and world-model pipelines. Unlike layer-structured neural networks, PBNR has primitive-indexed dependencies: each view reads and updates only a sparse, view-dependent subset of mutable scene state. As large scenes require distributed training and optimized renderers reduce per-view...
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04
Fine-grained Computation-Communication Overlap via Tile-level Signaling and Scheduling for Mixture-of-Experts
Minyu Cui, Anna Wingkvist, Morgan Ericsson
cs.DC · cs.AI
Mixture-of-Experts (MoE) architectures increase model capacity without proportionally increasing computation cost and have become a key building block for scaling large language models (LLMs) to trillion-parameter regimes. Efficient deployment of these MoE models relies on distributed execution across multiple GPUs, where each MoE layer involves two all-to-all communications: dispatching tokens to expert ranks and returning the expert outputs...
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