1 citations · 2 across the 10 of their papers we have counts for
10 papers
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…
Reliable and Resilient Collective Communication Library for LLM Training and Serving
Wei Wang, Nengneng Yu, Sixian Xiong +1
Modern ML training and inference now span tens to tens of thousands of GPUs, where network faults can waste 10--15\% of GPU hours due to slow recovery. Common network errors and li…
AutoTailor: Automatic and Efficient Adaptive Model Deployment for Diverse Edge Devices
Mengyang Liu, Chenyu Lu, Haodong Tian +7
On-device machine learning (ML) has become a fundamental component of emerging mobile applications. Adaptive model deployment delivers efficient inference for heterogeneous device…
Optimizing Distributed Deployment of Mixture-of-Experts Model Inference in Serverless Computing
Mengfan Liu, Wei Wang, Chuan Wu
With the advancement of serverless computing, running machine learning (ML) inference services over a serverless platform has been advocated, given its labor-free scalability and c…
Echo: Simulating Distributed Training At Scale
Yicheng Feng, Yuetao Chen, Kaiwen Chen +7
Simulation offers unique values for both enumeration and extrapolation purposes, and is becoming increasingly important for managing the massive machine learning (ML) clusters and…