12 citations · 21 across the 6 of their papers we have counts for
6 papers
Escape from Callback Hell! A New Programming Paradigm for Network Simulation
Yuanyi Zhu, Zijian Li, Xin Ai +3
Network simulation plays a crucial role in both networking research and industry. Existing commonly-used Discrete Event Simulations (DES) are based on callback mechanisms for discr…
Coral: Cost-Efficient Multi-LLM Serving over Heterogeneous Cloud GPUs
Yixuan Mei, Zikun Li, Zixuan Chen +5
The landscape of large language models (LLMs) has become increasingly fragmented, with no single model dominating the field. Meanwhile, cloud providers offer a diverse array of mid…
Rina: Enhancing Ring-AllReduce with In-network Aggregation in Distributed Model Training
Zixuan Chen, Xuandong Liu, Minglin Li +7
Parameter Server (PS) and Ring-AllReduce (RAR) are two widely utilized synchronization architectures in multi-worker Deep Learning (DL), also referred to as Distributed Deep Learni…
OSP: Boosting Distributed Model Training with 2-stage Synchronization
Zixuan Chen, Lei Shi, Xuandong Liu +3
Distributed deep learning (DDL) is a promising research area, which aims to increase the efficiency of training deep learning tasks with large size of datasets and models. As the c…
SDT: A Low-cost and Topology-reconfigurable Testbed for Network Research
Zixuan Chen, Zhigao Zhao, Zijian Li +3
Network experiments are essential to network-related scientific research (e.g., congestion control, QoS, network topology design, and traffic engineering). However, (re)configuring…
Boosting Distributed Machine Learning Training Through Loss-tolerant Transmission Protocol
Zixuan Chen, Lei Shi, Xuandong Liu +3
Distributed Machine Learning (DML) systems are utilized to enhance the speed of model training in data centers (DCs) and edge nodes. The Parameter Server (PS) communication archite…