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cs.PF2026
Compass: Dissecting Communication and Computation Operators for Efficient LLM Training
Guangyu Xiang, Lin Zhang, Haoxuan Yu +3
Overlapping communication and computation operators is a common practice to hide communication overheads, accelerating large language models (LLMs) training on GPU clusters. Existi…
cs.DC2026
ZipCCL: Efficient Lossless Data Compression of Communication Collectives for Accelerating LLM Training
Wenxiang Lin, Xinglin Pan, Ruibo Fan +2
Communication has emerged as a critical bottleneck in the distributed training of large language models (LLMs). While numerous approaches have been proposed to reduce communication…
cs.DC2026
ZipServ: Fast and Memory-Efficient LLM Inference with Hardware-Aware Lossless Compression
Ruibo Fan, Xiangrui Yu, Xinglin Pan +5
Lossless model compression holds tremendous promise for alleviating the memory and bandwidth bottlenecks in bit-exact Large Language Model (LLM) serving. However, existing approach…