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cs.DC2026
CommBench: Can LLMs Write Correct and Efficient GPU Communication Code?
Shuang Ma, Yuyi Li, Yihan Zhang +12
Training and serving large language models (LLMs) rely heavily on high-performance GPU communication, yet implementing efficient GPU communication primitives requires deep expertis…
cs.DC2026
UCCL-Zip: Lossless Compression Supercharged GPU Communication
Shuang Ma, Chon Lam Lao, Zhiying Xu +8
The rapid growth of large language models (LLMs) has made GPU communication a critical bottleneck. While prior work reduces communication volume via quantization or lossy compressi…
cs.DC2026
UCCL-EP: Portable Expert-Parallel Communication
Ziming Mao, Yihan Zhang, Chihan Cui +9
Mixture-of-Experts (MoE) workloads rely on expert parallelism (EP) to achieve high GPU efficiency. State-of-the-art EP communication systems such as DeepEP demonstrate strong perfo…