1 citations · 2 across the 10 of their papers we have counts for
6 papers · 1 filter
Tools-CC-Bench: a Benchmark Suite for Collective Communication with Compression in HPC and AI Workloads
Haozhe Fan, Wei Wang, Xingchen Liu +12
Distributed HPC and LLM workloads increasingly require efficient communication for scalability, yet growing data movement has become a major performance bottleneck. Communication c…
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…
HexiSeq: Accommodating Long Context Training of LLMs over Heterogeneous Hardware
Yan Liang, Youhe Jiang, Ran Yan +3
Long-context training of large language models (LLMs) is commonly distributed with Context Parallelism (CP) and Head Parallelism (HP), but existing training systems largely assume…
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…
DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training
Tianhao Hu, Xiangcheng Liu, Yuchun Miao +16
Reinforcement learning (RL) has become a critical paradigm for LLM post-training, yet the rollout phase -- accounting for 50--80% of total step time -- is bottlenecked by skewed ge…
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…