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cs.DC2026
StrataCL: Fabric-Native Communication Library for Production Supernodes
Tiancheng Hu, Jin Qin, Yuzheng Wang +14
Modern distributed AI workloads run across hundreds of accelerators, making communication a major bottleneck. Existing communication libraries remain largely buffer-centric because…
cs.DC2026
Exploiting Multicast for Accelerating Collective Communication
Chao Xu, Xu Zhang, Zihang Luo +5
Reducing collective communication latency is a critical goal for large model training and inference in both academia and industry. Many-to-many communications, such as AllGather an…
cs.DC2026
Relay Buffer Independent Communication over Pooled HBM for Efficient MoE Inference on Ascend
Tianlun Hu, Tiancheng Hu, Shengsheng Litang +8
Mixture-of-Experts (MoE) inference requires large-scale token exchange across devices, making dispatch and combine major bottlenecks in both prefill and decode. Beyond network tran…